Suppressive drug combinations and their potential to combat antibiotic resistance

Joint effects about antibiotics combined using with antibiotics or phytochemicals on Aeromonas hydrophila

Aeromonas hydrophila is highly prevalent in aquaculture animals and aquaculture environment. Due to the abuse of antibiotics, A. hydrophila can change the antibiotic resistance spectrum directly and affect human health indirectly. The use of combined drugs replacement therapy and the long-term coexistence with drug-resistant bacteria are the reality that human beings have to face in dealing with the problem of antibiotic resistance in the future. This study showed the characteristics and trends through abundant results of combined effects related with the combinations of antibiotic and the combinations of antibiotic and phytochemical on A. hydrophila, and revealed the antagonism probability of combinations of antibiotic and phytochemical is significantly higher than that of the combinations of antibiotic. Meanwhile, the combinations of antibiotic and phytochemical could protect the host cells which also achieved the same effectiveness as combination of antibiotics, and the enrichment pathway was proved to be relatively discrete. In addition, the possible mechanism about the reverse "U" shape of the combined effect curve on wild/antibiotic-resistant bacteria was clarified, and it was confirmed that the antagonism for the combinations of antibiotic and phytochemical might has the significance in inhibiting the evolution of bacterial resistance mutations. This study was aims to provide theoretical basis and some clues for the antibiotic resistance control associated with A. hydrophila.

Evolutionary druggability for low-dimensional fitness landscapes toward new metrics for antimicrobial applications

The term 'druggability' describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and 7 β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ('variant vulnerability'), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ('drug applicability'). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

Use of multiple pharmacodynamic measures to deconstruct the Nix-TB regimen in a short-course murine model of tuberculosis

A major challenge for tuberculosis (TB) drug development is to prioritize promising combination regimens from a large and growing number of possibilities. This includes demonstrating individual drug contributions to the activity of higher-order combinations. A BALB/c mouse TB infection model was used to evaluate the contributions of each drug and pairwise combination in the clinically relevant Nix-TB regimen [bedaquiline-pretomanid-linezolid (BPaL)] during the first 3 weeks of treatment at human equivalent doses. The rRNA synthesis (RS) ratio, an exploratory pharmacodynamic (PD) marker of ongoing Mycobacterium tuberculosis rRNA synthesis, together with solid culture CFU counts and liquid culture time to positivity (TTP) were used as PD markers of treatment response in lung tissue; and their time-course profiles were mathematically modeled using rate equations with pharmacologically interpretable parameters. Antimicrobial interactions were quantified using Bliss independence and Isserlis formulas. Subadditive (or antagonistic) and additive effects on bacillary load, assessed by CFU and TTP, were found for bedaquiline-pretomanid and linezolid-containing pairs, respectively. In contrast, subadditive and additive effects on rRNA synthesis were found for pretomanid-linezolid and bedaquiline-containing pairs, respectively. Additionally, accurate predictions of the response to BPaL for all three PD markers were made using only the single-drug and pairwise effects together with an assumption of negligible three-way drug interactions. The results represent an experimental and PD modeling approach aimed at reducing combinatorial complexity and improving the cost-effectiveness of in vivo systems for preclinical TB regimen development.

Automated and miniaturized screening of antibiotic combinations via robotic-printed combinatorial droplet platform

Antimicrobial resistance (AMR) has become a global health crisis in need of novel solutions. To this end, antibiotic combination therapies, which combine multiple antibiotics for treatment, have attracted significant attention as a potential approach for combating AMR. To facilitate advances in antibiotic combination therapies, most notably in investigating antibiotic interactions and identifying synergistic antibiotic combinations however, there remains a need for automated high-throughput platforms that can create and examine antibiotic combinations on-demand, at scale, and with minimal reagent consumption. To address these challenges, we have developed a Robotic-Printed Combinatorial Droplet (RoboDrop) platform by integrating a programmable droplet microfluidic device that generates antibiotic combinations in nanoliter droplets in automation, a robotic arm that arranges the droplets in an array, and a camera that images the array of thousands of droplets in parallel. We further implement a resazurin-based bacterial viability assay to accelerate our antibiotic combination testing. As a demonstration, we use RoboDrop to corroborate two pairs of antibiotics with known interactions and subsequently identify a new synergistic combination of cefsulodin, penicillin, and oxacillin against a model E. coli strain. We therefore envision RoboDrop becoming a useful tool to efficiently identify new synergistic antibiotic combinations toward combating AMR.

Synergistic Drug Combinations Promote the Development of Resistance in Acute Myeloid Leukemia

Combination therapy is an important part of cancer treatment and is often employed to overcome or prevent drug resistance. Preclinical screening strategies often prioritize synergistic drug combinations; however, studies of antibiotic combinations show that synergistic drug interactions can accelerate the emergence of resistance because resistance to one drug depletes the effect of both. In this study, we aimed to determine whether synergy drives the development of resistance in cancer cell lines using live-cell imaging. Consistent with prior models of tumor evolution, we found that when controlling for activity, drug synergy is associated with increased probability of developing drug resistance. We demonstrate that these observations are an expected consequence of synergy: the fitness benefit of resisting a drug in a combination is greater in synergistic combinations than in nonsynergistic combinations. These data have important implications for preclinical strategies aiming to develop novel combinations of cancer therapies with robust and durable efficacy.

SIGNIFICANCE

Preclinical strategies to identify combinations for cancer treatment often focus on identifying synergistic combinations. This study shows that in AML cells combinations that rely on synergy can increase the likelihood of developing resistance, suggesting that combination screening strategies may benefit from a more holistic approach rather than focusing on drug synergy. See related commentary by Bhola and Letai, p. 81. This article is featured in Selected Articles from This Issue, p. 80.

Fluorescent Ligand Equilibrium Displacement: A High-Throughput Method for Identification of FMN Riboswitch-Binding Small Molecules

Antibiotic resistance remains a pressing global concern, with most antibiotics targeting the bacterial ribosome or a limited range of proteins. One class of underexplored antibiotic targets is bacterial riboswitches, structured RNA elements that regulate key biosynthetic pathways by binding a specific ligand. We developed a methodology termed Fluorescent Ligand Equilibrium Displacement (FLED) to rapidly discover small molecules that bind the flavin mononucleotide (FMN) riboswitch. FLED leverages intrinsically fluorescent FMN and the quenching effect on RNA binding to create a label-free, in vitro method to identify compounds that can bind the apo population of riboswitch in a system at equilibrium. The response difference between known riboswitch ligands and controls demonstrates the robustness of the method for high-throughput screening. An existing drug discovery library that was screened using FLED resulted in a final hit rate of 0.67%. The concentration response of each hit was determined and revealed a variety of approximate effective concentration values. Our preliminary screening data support the use of FLED to identify small molecules for medicinal chemistry development as FMN riboswitch-targeted antibiotic compounds. This robust, label-free, and cell-free method offers a strong alternative to other riboswitch screening methods and can be adapted to a variety of laboratory setups.

In vitro activity of fidaxomicin and combinations of fidaxomicin with other antibiotics against Clostridium perfringens strains isolated from dogs and cats

BACKGROUND

Previous studies have demonstrated that fidaxomicin, a macrocyclic lactone antibiotic used to treat recurrent Clostridioides difficile-associated diarrhea, also displays potent in vitro bactericidal activity against Clostridium perfringens strains isolated from humans. However, to date, there is no data on the susceptibility to fidaxomicin of C. perfringens strains of animal origin. On the other hand, although combination therapy has become popular in human and veterinary medicine, limited data are available on the effects of antibiotic combinations on C. perfringens. We studied the in vitro response of 21 C. perfringens strains obtained from dogs and cats to fidaxomicin and combinations of fidaxomicin with six other antibiotics.

RESULTS

When tested by an agar dilution method, fidaxomicin minimum inhibitory concentrations (MICs) ranged between 0.004 and 0.032 µg/ml. Moreover, the results of Etest-based combination assays revealed that the incorporation of fidaxomicin into the test medium at a concentration equivalent to half the MIC significantly increased the susceptibility of isolates to metronidazole and erythromycin in 71.4% and 61.9% of the strains, respectively, and the susceptibility to clindamycin, imipenem, levofloxacin, and vancomycin in 42.9-52.4% of the strains. In contrast, ¼ × MIC concentrations of fidaxomicin did not have any effect on levofloxacin and vancomycin MICs and only enhanced the effects of clindamycin, erythromycin, imipenem, and metronidazole in ≤ 23.8% of the tested strains.

CONCLUSIONS

The results of this study demonstrate that fidaxomicin is highly effective against C. perfringens strains of canine and feline origin. Although fidaxomicin is currently considered a critically important antimicrobial that has not yet been licensed for veterinary use, we consider that the results reported in this paper provide useful baseline data to track the possible emergence of fidaxomicin resistant strains of C. perfringens in the veterinary setting.

Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications

The term "druggability" describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and seven β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ("variant vulnerability"), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ("drug applicability"). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G × G × E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

Fused Triazinobenzimidazoles Bearing Heterocyclic Moiety: Synthesis, Structure Investigations, and In Silico and In Vitro Biological Activity

[1,3,5]Triazino[1,2-a]benzimidazole-2-amines bearing heterocyclic moiety in 4-position were synthesized. The compounds were characterized by elemental analysis, IR, ¹H-NMR, ¹³C-NMR, and HRMS spectroscopy. The molecular geometry and electron structure of these molecules were theoretically studied using density functional theory (DFT) methods. The molecular structure of the synthesized fused triazinobenzimidazole was confirmed to correspond to the 3,4-dihydrotriazinobenzimidazole structure through the analysis of spectroscopic NMR data and DFT calculations. The antinematodic activity was evaluated in vitro on isolated encapsulated muscle larvae (ML) of Trichinella spiralis. The results showed that the tested triazinobenzimidazoles exhibit significantly higher efficiency than the conventional drug used to treat trichinosis, albendazole, at a concentration of 50 μg/mL. The compound 3c substituted with a thiophen-2-yl moiety exhibited the highest anthelmintic activity, with a larvicidal effect of 58.41% at a concentration of 50 μg/mL after 24 h of incubation. Following closely behind, the pyrrole analog 3f demonstrated 49.90% effectiveness at the same concentration. The preliminary structure-anti-T. spiralis activity relationship (SAR) of the analogues in the series was discussed. The cytotoxicity of the benzimidazole derivatives against two normal fibroblast cells (3T3 and CCL-1) and two cancer human cell lines (MCF-7 breast cancer cells and chronic myeloid leukemia cells AR-230) was evaluated using the MTT-dye reduction assay. The screening results indicated that the compounds showed no cytotoxicity against the tested cell lines. An in silico study of the physicochemical and pharmacokinetic characteristics of the novel synthesized fused triazinobenzimidazoles showed that they were characterized by a significant degree of drug-likeness and optimal properties for anthelmintic agents.

Effects of antibiotic interaction on antimicrobial resistance development in wastewater

While wastewater is understood to be a critically important reservoir of antimicrobial resistance due to the presence of multiple antibiotic residues from industrial and agricultural runoff, there is little known about the effects of antibiotic interactions in the wastewater on the development of resistance. We worked to fill this gap in quantitative understanding of antibiotic interaction in constant flow environments by experimentally monitoring E. coli populations under subinhibitory concentrations of combinations of antibiotics with synergistic, antagonistic, and additive interactions. We then used these results to expand our previously developed computational model to account for the effects of antibiotic interaction. We found that populations grown under synergistic and antagonistic antibiotic conditions exhibited significant differences from predicted behavior. E. coli populations grown with synergistically interacting antibiotics developed less resistance than predicted, indicating that synergistic antibiotics may have a suppressive effect on resistance development. Furthermore E. coli populations grown with antagonistically interacting antibiotics showed an antibiotic ratio-dependent development of resistance, suggesting that not only antibiotic interaction, but relative concentration is important in predicting resistance development. These results provide critical insight for quantitatively understanding the effects of antibiotic interactions in wastewater and provide a basis for future studies in modelling resistance in these environments.

Meta-analysis of three-stressor combinations on population-level fitness reveal substantial higher-order interactions

Although natural populations are typically subjected to multiple stressors, most past research has focused on single-stressor and two-stressor interactions, with little attention paid to higher-order interactions among three or more stressors. However, higher-order interactions increasingly appear to be widespread. Consequently, we used a recently introduced and improved framework to re-analyze higher-order ecological interactions. We conducted a literature review of the last 100 years (1920-2020) and reanalyzed 142 ecological three-stressor interactions on species' populations from 38 published papers; the vast majority of these studies were from the past 10 years. We found that 95.8 % (n = 136) of the three-stressor combinations had either not been categorized before or resulted in different interactions than previously reported. We also found substantial levels of emergent properties-interactions that are not due to strong pairwise interactions within the combination but rather uniquely due to all three stressors being combined. Calculating net interactions-the overall accounting for all possible interactions within a combination including the emergent and all pairwise interactions-we found that the most prevalent interaction type is antagonism, corresponding to a smaller than expected effect based on single stressor effects. In contrast, for emergent interactions, the most prevalent interaction type is synergistic, resulting in a larger than expected effect based on single stressor effects. Additionally, we found that hidden suppressive interactions-where a pairwise interaction is suppressed by a third stressor-are found in the majority of combinations (74 %). Collectively, understanding multiple stressor interactions through applying an appropriate framework is crucial for answering fundamental questions in ecology and has implications for conservation biology and population management. Crucially, identifying emergent properties can reveal hidden suppressive interactions that could be particularly important for the ecological management of at-risk populations.

Collateral sensitivity between tetracyclines and aminoglycosides constrains resistance evolution in carbapenem-resistant Klebsiella pneumoniae

AIMS

The acquisition of resistance to one antibiotic may confer an increased sensitivity to another antibiotic in bacteria, which is an evolutionary trade-off between different resistance mechanisms, defined as collateral sensitivity (CS). Exploiting the role of CS in treatment design could be an effective method to suppress or even reverse resistance evolution.

METHODS

Using experimental evolution, we systematically studied the CS between aminoglycosides and tetracyclines in carbapenem-resistant Klebsiella pneumoniae (CRKP) and explored the underlying mechanisms through genomic and transcriptome analyses. The application of CS-based therapies for resistance suppression, including combination therapy and alternating antibiotic therapy, was further evaluated in vitro and in vivo.

RESULTS

Reciprocal CS existed between tetracyclines and aminoglycosides in CRKP. The increased sensitivity of aminoglycoside-resistant strains to tetracyclines was associated with the alteration of bacterial membrane potential, whereas the unbalanced oxidation-reduction process of tetracycline-resistant strains may lead to an increased bacterial sensitivity to aminoglycosides. CS-based combination therapy could efficiently constrain the evolution of CRKP resistance in vitro and in vivo. In addition, alternating antibiotic therapy can re-sensitize CRKP to previously resistant drugs, thereby maintaining the trade-off.

CONCLUSIONS

These results provide new insights into constraining the evolution of CRKP resistance through CS-based therapies.

Effects of Antibiotic Interaction on Antimicrobial Resistance Development in Wastewater

While wastewater is understood to be a critically important reservoir of antimicrobial resistance due to the presence of multiple antibiotic residues from industrial and agricultural runoff, there is little known about the effects of antibiotic interactions in the wastewater on the development of resistance. We worked to fill this gap in quantitative understanding of antibiotic interaction in constant flow environments by experimentally monitoring E. coli populations under subinhibitory concentrations of combinations of antibiotics with synergistic, antagonistic, and additive interactions. We then used these results to expand our previously developed computational model to account for the complex effects of antibiotic interaction. We found that while E. coli populations grown in additively interacting antibiotic combinations grew predictably according to the previously developed model, those populations grown under synergistic and antagonistic antibiotic conditions exhibited significant differences from predicted behavior. E. coli populations grown in the condition with synergistically interacting antibiotics developed less resistance than predicted, indicating that synergistic antibiotics may have a suppressive effect on antimicrobial resistance development. Furthermore E. coli populations grown in the condition with antagonistically interacting antibiotics showed an antibiotic ratio-dependent development of resistance, suggesting that not only antibiotic interaction, but relative concentration is important in predicting resistance development. These results provide critical insight for quantitatively understanding the effects of antibiotic interactions in wastewater and provide a basis for future studies in modelling resistance in these environments.

Importance

Antimicrobial resistance (AMR) is a growing global threat to public health expected to impact 10 million people by 2050, driving mortality rates globally and with a disproportionate effect on low- and middle-income countries. Communities in proximity to wastewater settings and environmentally contaminated surroundings are at particular risk due to resistance stemming from antibiotic residues from industrial and agricultural runoff. Currently, there is a limited quantitative and mechanistic understanding of the evolution of AMR in response to multiple interacting antibiotic residues in constant flow environments. Using an integrated computational and experimental methods, we find that interactions between antibiotic residues significantly affect the development of resistant bacterial populations.

Coping with the ESKAPE pathogens: Evolving strategies, challenges and future prospects

Globally, the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are the major cause of nosocomial infections. These pathogens are multidrug resistant, and their negative impacts have brought serious health challenges and economic burden on many countries worldwide. Thus, this narrative review exploits different emerging alternative therapeutic strategies including combination antibiotics, antimicrobial peptides ((AMPs), bacteriophage and photodynamic therapies used in the treatment of the ESKAPE pathogens, their merits, limitations, and future prospects. Our findings indicate that ESKAPE pathogens exhibit resistance to drug using different mechanisms including drug inactivation by irreversible enzyme cleavage, drug-binding site alteration, diminution in permeability of drug or drug efflux increment to reduce accumulation of drug as well as biofilms production. However, the scientific community has shown significant interest in using these novel strategies with numerous benefits although they have some limitations including but not limited to instability and toxicity of the therapeutic agents, or the host developing immune response against the therapeutic agents. Thus, comprehension of resistance mechanisms of these pathogens is necessary to further develop or modify these approaches in order to overcome these health challenges including the barriers of bacterial resistance.

Anti-Staphylococcal Activities of Rosmarinus officinalis and Myrtus communis Essential Oils through ROS-Mediated Oxidative Stress

Rosmarinus officinalis and Myrtus communis essential oils (EOs) are well-known for their ethno-pharmaceutical properties. In the present study, we have analyzed the chemical composition of both EOs by gas chromatography-mass spectrometry. Then we assessed their antibacterial, antibiofilm, and anti-virulence actions against the opportunistic pathogen Staphylococcus aureus. The cytotoxic effect of agents tested against this bacterium was investigated by monitoring reactive oxygen-species (ROS) generation and antioxidant-enzyme (catalase) production. Regarding the antistaphylococcal effects, our results showed antibacterial efficacy of both Eos and their combination, where the minimum inhibitory concentrations ranged between 0.7 and 11.25 mg/mL. A combination of tested agents showed the highest anti-hemolytic and anti-protease effects. Additionally, association between EOs displayed more potency against the development of biofilm performed by S. aureus, with percentage of removal reaching 74%. The inhibitory impacts of EOs on S. aureus virulence factors were discovered to be concentration-dependent. Furthermore, our results provide insight on the abilities of R. officinalis and M. communis EOs, as well as their potential in combination, to generate ROS and affect oxidative stress enzyme catalase in S. aureus, leading to their antagonistic effect against this pathogen.

Resistance-resistant antibacterial treatment strategies

Antibiotic resistance is a major danger to public health that threatens to claim the lives of millions of people per year within the next few decades. Years of necessary administration and excessive application of antibiotics have selected for strains that are resistant to many of our currently available treatments. Due to the high costs and difficulty of developing new antibiotics, the emergence of resistant bacteria is outpacing the introduction of new drugs to fight them. To overcome this problem, many researchers are focusing on developing antibacterial therapeutic strategies that are "resistance-resistant"-regimens that slow or stall resistance development in the targeted pathogens. In this mini review, we outline major examples of novel resistance-resistant therapeutic strategies. We discuss the use of compounds that reduce mutagenesis and thereby decrease the likelihood of resistance emergence. Then, we examine the effectiveness of antibiotic cycling and evolutionary steering, in which a bacterial population is forced by one antibiotic toward susceptibility to another antibiotic. We also consider combination therapies that aim to sabotage defensive mechanisms and eliminate potentially resistant pathogens by combining two antibiotics or combining an antibiotic with other therapeutics, such as antibodies or phages. Finally, we highlight promising future directions in this field, including the potential of applying machine learning and personalized medicine to fight antibiotic resistance emergence and out-maneuver adaptive pathogens.

Sequential antibiotic therapy in the laboratory and in the patient

Laboratory experiments suggest that rapid cycling of antibiotics during the course of treatment could successfully counter resistance evolution. Drugs involving collateral sensitivity could be particularly suitable for such therapies. However, the environmental conditions in vivo differ from those in vitro. One key difference is that drugs can be switched abruptly in the laboratory, while in the patient, pharmacokinetic processes lead to changing antibiotic concentrations including periods of dose overlaps from consecutive administrations. During such overlap phases, drug-drug interactions may affect the evolutionary dynamics. To address the gap between the laboratory and potential clinical applications, we set up two models for comparison-a 'laboratory model' and a pharmacokinetic-pharmacodynamic 'patient model'. The analysis shows that in the laboratory, the most rapid cycling suppresses the bacterial population always at least as well as other regimens. For patient treatment, however, a little slower cycling can sometimes be preferable if the pharmacodynamic curve is steep or if drugs interact antagonistically. When resistance is absent prior to treatment, collateral sensitivity brings no substantial benefit unless the cell division rate is low and drug cycling slow. By contrast, drug-drug interactions strongly influence the treatment efficiency of rapid regimens, demonstrating their importance for the optimal choice of drug pairs.

Antibiotic combinations reduce Staphylococcus aureus clearance

The spread of antibiotic resistance is attracting increased attention to combination-based treatments. Although drug combinations have been studied extensively for their effects on bacterial growth^1–11, much less is known about their effects on bacterial long-term clearance, especially at cidal, clinically relevant concentrations^12–14. Here, using en masse microplating and automated image analysis, we systematically quantify Staphylococcus aureus survival during prolonged exposure to pairwise and higher-order cidal drug combinations. By quantifying growth inhibition, early killing and longer-term population clearance by all pairs of 14 antibiotics, we find that clearance interactions are qualitatively different, often showing reciprocal suppression whereby the efficacy of the drug mixture is weaker than any of the individual drugs alone. Furthermore, in contrast to growth inhibition^6–10 and early killing, clearance efficacy decreases rather than increases as more drugs are added. However, specific drugs targeting non-growing persisters^15–17 circumvent these suppressive effects. Competition experiments show that reciprocal suppressive drug combinations select against resistance to any of the individual drugs, even counteracting methicillin-resistant Staphylococcus aureus both in vitro and in a Galleria mellonella larva model. As a consequence, adding a β-lactamase inhibitor that is commonly used to potentiate treatment against β-lactam-resistant strains can reduce rather than increase treatment efficacy. Together, these results underscore the importance of systematic mapping the long-term clearance efficacy of drug combinations for designing more-effective, resistance-proof multidrug regimes.

Different pairs of antibiotics show qualitatively different bacterial clearance interactions—some pairs show reciprocal suppression whereby the drug mixture efficacy is weaker than the individual drugs alone, and the clearance efficacy decreases as more drugs are added.

A Retrospective Study of Risk Factors, Mortality, and Treatment Outcomes for Infections with Carbapenemase-Producing Enterobacterales in a Tertiary Hospital in Havana, Cuba

(1) Background: The spread of carbapenem-resistant Enterobacterales in hospitals constitutes an important epidemiological and therapeutic problem that especially affects vulnerable patients such as perioperative patients. (2) Methods: We conducted a descriptive, observational, retrospective case-control study of patients infected with carbapenemase-producing carbapenem-resistant Enterobacterales (CP-CRE) and carbapenem-susceptible Enterobacterales during the perioperative period in a tertiary hospital. (3) Results: Metallo-β-lactamase was detected in all 124 CRE isolates, with NDM-type carbapenemase being dominant, while 3 isolates coproduced KPC-type enzyme and showed high resistance rates against all antibiotics except colistin (25.2%). By analyzing the risk factors for infection, steroid use (OR: 3.22, p < 0.01), prior use of two or more antibiotics (OR: 4.04, p = 0.01), prior use of broad-spectrum cephalosporins (OR: 2.40, p = 0.04), and prior use of carbapenem (OR: 4.77, p = 0.03) were found to be independent risk factors for CP-CRE infection. In addition, in this study, we observed that the clinical outcomes of bloodstream infections and pneumonia associated with CP-CRE posed higher mortality risks. However, by analyzing the associations between treatment options and mortality, it was found that, in bloodstream infections caused by CP-CRE, colistin-based regimens showed a significant advantage (PR = 0.40, p = 0.03). (4) Conclusions: High mortality is associated with nosocomial infections in the perioperative period caused by carbapenemase-producing Enterobacterales, the dissemination of which in health care settings in Cuba remains a public health challenge.

Identification of quinoline derivatives as growth inhibitors of MDR pathogen Klebsiella pneumoniae

Aims: This study was aimed to identify compounds with significant inhibitory potential against multidrug-resistant (MDR), multidrug-sensitive, and clinical isolates of Klebsiella pneumoniae. Materials & methods: Antibacterial activity of the nitroquinoline derivatives was assessed by micro-plate Alamar Blue assay. Results: Nitroquinoline derivatives 9, 11 and 14 showed inhibitory activity against MDR K. pneumoniae. Docking studies of these compounds with topoisomerase IV of K. pneumonia indicated the interactions of these compounds at the active site residues of enzyme near to cofactor (Mg⁺²). Furthermore, compound 11 was identified as a reactive oxygen species (ROS) inducer. None of the compounds showed hemolytic effect. Conclusion: This study was designed to identify compounds active against MDR K. pneumoniae which causes infections, such as pneumonia and urinary tract infections.

Synergistic Anti-Inflammatory Activity of Ginger and Turmeric Extracts in Inhibiting Lipopolysaccharide and Interferon-γ-Induced Proinflammatory Mediators

This study aims to investigate the combined anti-inflammatory activity of ginger and turmeric extracts. By comparing the activities of individual and combined extracts in lipopolysaccharide and interferon-γ-induced murine RAW 264.7 cells, we demonstrated that ginger-turmeric combination was optimal at a specific ratio (5:2, w/w) in inhibiting nitric oxide, tumour necrosis factor and interleukin 6 with synergistic interaction (combination index < 1). The synergistic inhibitory effect on TNF was confirmed in human monocyte THP-1 cells. Ginger-turmeric combination (5:2, w/w) also upregulated nuclear factor erythroid 2−related factor 2 activity and heme oxygenase-1 protein expression. Additionally, 6-shogaol, 8-shogaol, 10-shogaol and curcumin were the leading compounds in reducing major proinflammatory mediators and cytokines, and a simplified compound combination of 6-s, 10-s and curcumin showed the greatest potency in reducing LPS-induced NO production. Our study provides scientific evidence in support of the combined use of ginger and turmeric to alleviate inflammatory processes.

Biofilm and hyphal inhibitory synergistic effects of phytoactives piperine and cinnamaldehyde against Candida albicans

Oral candidiasis, the most common mycotic infection of the human oral cavity is non-life-threatening yet if untreated may advance as systemic infections. Ability of Candida albicans to adapt sessile lifestyle imparts resistance to drugs and host immunity. Consequently, due to limited effectiveness of conventional antifungal treatment, novel therapeutic strategies are required. In the present study, synergistic interaction of phytochemicals, piperine and cinnamaldehyde against the biofilm and hyphal of C. albicans was evaluated. Minimum inhibitory concentration (MIC) and biofilm inhibitory concentration (BIC) of piperine and cinnamaldehyde against C. albicans were analysed through microbroth dilution assay and crystal violet staining method, respectively. Combinatorial biofilm and hyphal inhibitory effect were investigated through checkerboard assay. In vitro results were validated through gene expression analysis. BIC of piperine and cinnamaldehyde was determined to be 32 µg/mL and 64 µg/mL, respectively. Interaction between these two phytocomponents was found to be synergistic and six different synergistic antibiofilm combinations were identified. Microscopic analysis of biofilm architecture also evidenced the biofilm and surface adherence inhibitory potential of piperine and cinnamaldehyde combinations. Phenotypic switching between yeast and hyphal morphological forms was influenced by synergistic combinations. qPCR analysis corroborated the results of in vitro activities. nrg1 and trp1, the negative transcriptional regulators of filamentous growth were upregulated whereas other genes that are involved in biofilm formation, filamentous growth, adhesion etc were found to be downregulated. These proficient phytochemical combinations provide a new therapeutic avenue for the treatment of biofilm associated oral candidiasis and to combat the recurrent infections due to antibiotic resistance.

Naturally-Sourced Antibacterial Polymeric Nanomaterials with Special Reference to Modified Polymer Variants

Despite the recent advancements in treating bacterial infections, antibiotic resistance (AR) is still an emerging issue. However, polymeric nanocarriers have offered unconventional solutions owing to their capability of exposing more functional groups, high encapsulation efficiency (EE) and having sustained delivery. Natural polymeric nanomaterials (NMs) are contemplated one of the most powerful strategies in drug delivery (DD) in terms of their safety, biodegradability with almost no side effects. Every nanostructure is tailored to enhance the system functionality. For example, cost-effective copper NPs could be generated in situ in cellulose sheets, demonstrating powerful antibacterial prospects for food safety sector. Dendrimers also have the capacity for peptide encapsulation, protecting them from proteolytic digestion for prolonged half life span. On the other hand, the demerits of naturally sourced polymers still stand against their capacities in DD. Hence, Post-synthetic modification of natural polymers could play a provital role in yielding new hybrids while retaining their biodegradability, which could be suitable for building novel super structures for DD platforms. This is the first review presenting the contribution of natural polymers in the fabrication of eight polymeric NMs including particulate nanodelivery and nanofabrics with antibacterial and antibiofilm prospects, referring to modified polymer derivatives to explore their full potential for obtaining sustainable DD products.

The chemical ecology of the fungus-farming termite symbiosis

Covering: September 1972 to December 2020Explorations of complex symbioses have often elucidated a plethora of previously undescribed chemical compounds that may serve ecological functions in signalling, communication or defence. A case in point is the subfamily of termites that cultivate a fungus as their primary food source and maintain complex bacterial communities, from which a series of novel compound discoveries have been made. Here, we summarise the origins and types of 375 compounds that have been discovered from the symbiosis over the past four decades and discuss the potential for synergistic actions between compounds within the complex chemical mixtures in which they exist. We go on to highlight how vastly underexplored the diversity and geographic distribution of the symbiosis is, which leaves ample potential for natural product discovery of compounds of both ecological and medical importance.

Comparison of Joint Effect of Acute and Chronic Toxicity for Combined Assessment of Heavy Metals on Photobacterium sp.NAA-MIE

Predicting the crucial effect of single metal pollutants against the aquatic ecosystem has been highly debatable for decades. However, dealing with complex metal mixtures management in toxicological studies creates a challenge, as heavy metals may evoke greater toxicity on interactions with other constituents rather than individually low acting concentrations. Moreover, the toxicity mechanisms are different between short term and long term exposure of the metal toxicant. In this study, acute and chronic toxicity based on luminescence inhibition assay using newly isolated Photobacterium sp.NAA-MIE as the indicator are presented. Photobacterium sp.NAA-MIE was exposed to the mixture at a predetermined ratio of 1:1. TU (Toxicity Unit) and MTI (Mixture Toxic Index) approach presented the mixture toxicity of Hg²⁺ + Ag⁺, Hg²⁺ + Cu²⁺, Ag⁺ + Cu²⁺, Hg²⁺ + Ag⁺ + Cu²⁺, and Cd²⁺ + Cu²⁺ showed antagonistic effect over acute and chronic test. Binary mixture of Cu²⁺ + Zn²⁺ was observed to show additive effect at acute test and antagonistic effect at chronic test while mixture of Ni²⁺ + Zn²⁺ showing antagonistic effect during acute test and synergistic effect during chronic test. Thus, the strain is suitable and their use as bioassay to predict the risk assessment of heavy metal under acute toxicity without abandoning the advantage of chronic toxicity extrapolation.

Therapeutic Efficacy of Albendazole and Mefloquine Alone or in Combination Against Early and Late Stages of Trichinella Spiralis Infection in Mice

This study aimed to determine the effectiveness of mefl oquine alone or combined with albendazole in reduced doses against T. spiralis infection. One hundred and twenty albino mice were orally infected with 200 T. spiralis larvae/mouse. Drugs were administered during the enteral phase on days 1 to 3 and on the chronic phase on days 35 to 37 post-infection, and mice were sacrificed, respectively, at days 7 or 48 post-infection to count mature intestinal worms or encysted muscle larvae. The effect of the treatment on the histology of the target organs of each phase, intestine and diaphragm, was also evaluated. A signifi cant decrease in intestinal worms was found in all treated groups relative to the untreated control group at a peak of 93.7% in the combination albendazole-mefl oquine group. Results in all treated groups demonstrated a signifi cant decrease in muscle larvae relative to untreated control groups, achieving 86.2 % in the combined albendazole-mefl oquine group. There was a marked improvement in the intestinal and muscular architecture in all treated groups compared to the non-treated control group. Notably, the albendazole-mefl oquine group showed an almost complete recovery. The combined albendazole-mefl oquine low dose regimen had the highest effect on reducing parasite burden and restoring normal histological architecture.

Comparison of Sodium Nitrite and ‘Natural’ Nitrite on the Inhibition of Spore Germination and Outgrowth of Clostridium sporogenes in Low- and High-Fat Frankfurters

In the US, sodium and potassium nitrite are regulated food preservatives that prevent the germination of Clostridium spores in cured and processed meats. In recent years, the use of vegetable-derived nitrite (i.e., vegetable nitrate fermented to nitrite) has been designated as ‘natural nitrite’ to accommodate natural meats that cannot use artificial ingredients, and such meat products can be labelled as having ‘no added preservatives’. This new status and labelling allowance for microbially-modified nitrite provides for a ‘clean label’ application of nitrite against the stigma of chemical ingredients and has found increased use within the processed meat industry. The objectives of this study were to examine Clostridium sporogenes as a pathogen-surrogate challenge organism and the use of vegetable (celery) nitrite to prevent spore germination in cooked meat products. A three-strain spore crop of C. sporogenes ATCC 3584, ATCC 19404 and ATCC BAA-2695 was applied during ingredient formulation of low and high-fat hotdogs that were divided into three sub-batches (control without nitrite, hotdogs with sodium nitrite, hotdogs with celery nitrite). In both low and high-fat processes, sodium nitrite was compared to hotdogs made with comparable levels of celery nitrite (156 ppm). All treatments were performed with duplicate trial replication and triplicate sample testing within each trial. Comparisons were analyzed by repeated measures analysis of variance to determine significant difference (p < 0.05) of time course treatments. In shelf-life assays, growth was inhibited at both 5 °C and 15 °C, even if nitrite was absent; however, spore germination and growth readily occurred at 35 °C. Comparison of nitrite effects was best evaluated at 35 °C as a permissive condition to examine the effects of nitrite treatments. Celery nitrite showed no significant difference from sodium nitrite when used in both low and high-fat hotdogs, and spore outgrowth was only observed after 2–3 days at 35 °C compared to hotdogs without nitrite. Application of bacteriocin preparations in the formulation that were effective against Listeria monocytogenes, and moderately inhibitory towards the 3-strain spore mixture of C. sporogenes, were not effective in spore control in manufactured hotdogs. The nitrite validation hotdog trials described herein demonstrates that (celery or sodium) nitrite may prevent Clostridium spore germination for 24–48 h even under permissive conditions to help keep processed meat safe.

Research Progress in Flavonoids as Potential Anticancer Drug Including Synergy with Other Approaches

BACKGROUND

In chemotherapy for cancer, conventional drugs aim to target the rapidly growing and dividing cells at the early stages. However, at an advanced stage, cancer cells become less susceptible because of the multidrug resistance and the recruitment of alternative salvage pathways for their survival. Besides, owing to target non-selectivity, healthy proliferating cells also become vulnerable to the damage. The combination therapies offered using flavonoids to cure cancer not only exert an additive effect against cancer cells by targetting supplementary cell carnage pathways but also hampers the drug resistance mechanisms. Thus, the review aims to discuss the potential and pharmacokinetic limitations of flavonoids in cancer treatment. Further successful synergistic studies reported using flavonoids to treat cancer has been described along with potential drug delivery systems.

METHODS

A literature search was done by exploring various online databases like Pubmed, Scopus, and Google Scholar with the specific keywords like "Anticancer drugs", "flavonoids", "oncology research", and "pharmacokinetics".

RESULTS

Dietary phytochemicals, mainly flavonoids, hinder cell signalling responsible for multidrug resistance and cancer progression, primarily targeting cancer cells sparing normal cells. Such properties establish flavonoids as a potential candidate for synergistic therapy. However, due to low absorption and high metabolism rates, the bioavailability of flavonoids becomes a challenge. Such challenges may be overcome using novel approaches like derivatization, and single or co-delivery nano-complexes of flavonoids with conventional drugs. These new approaches may improve the pharmacokinetic and pharmacodynamic of flavonoids.

CONCLUSION

This review highlights the application of flavonoids as a potential anticancer phytochemical class in combination with known anti-cancer drugs/nanoparticles. It also discusses flavonoid's pharmacokinetics and pharmacodynamics issues and ways to overcome such issues. Moreover, it covers successful methodologies employed to establish flavonoids as a safe and effective phytochemical class for cancer treatment.

Hidden suppressive interactions are common in higher-order drug combinations

The rapid increase of multi-drug resistant bacteria has led to a greater emphasis on multi-drug combination treatments. However, some combinations can be suppressive—that is, bacteria grow faster in some drug combinations than when treated with a single drug. Typically, when studying interactions, the overall effect of the combination is only compared with the single-drug effects. However, doing so could miss “hidden” cases of suppression, which occur when the highest order is suppressive compared with a lower-order combination but not to a single drug. We examined an extensive dataset of 5-drug combinations and all lower-order—single, 2-, 3-, and 4-drug—combinations. We found that a majority of all combinations—54%—contain hidden suppression. Examining hidden interactions is critical to understanding the architecture of higher-order interactions and can substantially affect our understanding and predictions of the evolution of antibiotic resistance under multi-drug treatments.

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Most instances of suppressive interactions are missed by standard methods

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A majority (54%) of all antibiotic combinations tested contain hidden suppression

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Identifying hidden suppression can affect what combinations should be used

Potential of substituted quinazolines to interact with multiple targets in the treatment of cancer

The efficacy of quinazoline-based antiglioma agents has been attributed to their effects on microtubule dynamics.1,2 The design, synthesis and biological evaluation of quinazolines as potent inhibitors of multiple intracellular targets, including microtubules and multiple RTKs, is described. In addition to the known ability of quinazolines 1 and 2 to cause microtubule depolymerization, they were found to be low nanomolar inhibitors of EGFR, VEGFR-2 and PDGFR-β. Low nanomolar inhibition of EGFR was observed for 1-3 and 9-10. Compounds 1 and 4 inhibited VEGFR-2 kinase with activity better than or equal to that of sunitinib. In addition, compounds 1 and 2 had similar potency to sunitinib in the CAM angiogenesis assay. Multitarget activities of compounds in the present study demonstrates that the quinazolines can affect multiple pathways and could lead to these agents having antitumor potential caused by their activity against multiple targets.

Network-principled deep generative models for designing drug combinations as graph sets

Motivation

Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery.

Results

We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed gene–gene, gene–disease and disease–disease networks. Novel attentional pooling is introduced here for learning disease representations from associated genes’ representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for disease-specific drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space.

Availability and implementation

https://github.com/Shen-Lab/Drug-Combo-Generator.

Supplementary information

Supplementary data are available at Bioinformatics online.

Antimicrobial Peptides and their Multiple Effects at Sub-Inhibitory Concentrations

The frequent occurrence of multidrug-resistant strains to conventional antimicrobials has led to a clear decline in antibiotic therapies. Therefore, new molecules with different mechanisms of action are extremely necessary. Due to their unique properties, antimicrobial peptides (AMPs) represent a valid alternative to conventional antibiotics and many of them have been characterized for their activity and cytotoxicity. However, the effects that these peptides cause at concentrations below the minimum growth inhibitory concentration (MIC) have yet to be fully analyzed along with the underlying molecular mechanism. In this mini-review, the ability of AMPs to synergize with different antibiotic classes or different natural compounds is examined. Furthermore, data on microbial resistance induction are reported to highlight the importance of antibiotic resistance in the fight against infections. Finally, the effects that sub-MIC levels of AMPs can have on the bacterial pathogenicity are summarized while showing how signaling pathways can be valid therapeutic targets for the treatment of infectious diseases. All these aspects support the high potential of AMPs as lead compounds for the development of new drugs with antibacterial and immunomodulatory activities.

Fighting microbial pathogens by integrating host ecosystem interactions and evolution

Successful therapies to combat microbial diseases and cancers require incorporating ecological and evolutionary principles. Drawing upon the fields of ecology and evolutionary biology, we present a systems-based approach in which host and disease-causing factors are considered as part of a complex network of interactions, analogous to studies of "classical" ecosystems. Centering this approach around empirical examples of disease treatment, we present evidence that successful therapies invariably engage multiple interactions with other components of the host ecosystem. Many of these factors interact nonlinearly to yield synergistic benefits and curative outcomes. We argue that these synergies and nonlinear feedbacks must be leveraged to improve the study of pathogenesis in situ and to develop more effective therapies. An eco-evolutionary systems perspective has surprising and important consequences, and we use it to articulate areas of high research priority for improving treatment strategies.

Weakest-Link Dynamics Predict Apparent Antibiotic Interactions in a Model Cross-Feeding Community

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen.

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, antagonism, or independence/additivity) in monoculture can be used to predict antibiotic interactions in an obligate cross-feeding coculture. Using our previously described weakest-link hypothesis, we hypothesized antibiotic interactions in coculture based on the interactions we observed in monoculture. We then compared our predictions to observed antibiotic interactions in coculture. We tested the interactions between 10 previously identified antibiotic combinations using checkerboard assays. Although our antibiotic combinations interacted differently than predicted in our monocultures, our monoculture results were generally sufficient to predict coculture patterns based solely on the weakest-link hypothesis. These results suggest that combination therapy for cross-feeding multispecies infections may be successfully designed based on antibiotic interaction patterns for their component species.

Evolution of generalist resistance to herbicide mixtures reveals a trade-off in resistance management

Intense selection by pesticides and antibiotics has resulted in a global epidemic of evolved resistance. In agriculture and medicine, using mixtures of compounds from different classes is widely accepted as optimal resistance management. However, this strategy may promote the evolution of more generalist resistance mechanisms. Here we test this hypothesis at a national scale in an economically important agricultural weed: blackgrass (Alopecurus myosuroides), for which herbicide resistance is a major economic issue. Our results reveal that greater use of herbicide mixtures is associated with lower levels of specialist resistance mechanisms, but higher levels of a generalist mechanism implicated in enhanced metabolism of herbicides with diverse modes of action. Our results indicate a potential evolutionary trade-off in resistance management, whereby attempts to reduce selection for specialist resistance traits may promote the evolution of generalist resistance. We contend that where specialist and generalist resistance mechanisms co-occur, similar trade-offs will be evident, calling into question the ubiquity of resistance management based on mixtures and combination therapies.

Mixtures of antibiotics or pesticides can help reduce the evolution of resistance to individual compounds. Here, Comont et al. show that in blackgrass, an important agricultural weed, herbicide mixtures do reduce specialized resistance but instead can select for a generalized resistance mechanism.

Novel Antibacterial Strategies for Combating Bacterial Multidrug Resistance

BACKGROUND

Antibacterial multidrug resistance has emerged as one of the foremost global problems affecting human health. The emergence of resistant infections with the increasing number of multidrug-resistant pathogens has posed a serious problem, which required innovative collaborations across multiple disciplines to address this issue.

METHODS

In this review, we will explain the mechanisms of bacterial multidrug resistance and discuss different strategies for combating it, including combination therapy, the use of novel natural antibiotics, and the use of nanotechnology in the development of efflux pump inhibitors.

RESULTS

While combination therapy will remain the mainstay of bacterial multi-drug resistance treatment, nanotechnology will play critical roles in the development of novel treatments in the coming years.

CONCLUSION

Nanotechnology provides an encouraging platform for the development of clinically relevant and practical strategies to overcome drug resistance in the future.

Effect of tolerance on the evolution of antibiotic resistance under drug combinations

Drug combinations are widely used in clinical practice to prevent the evolution of resistance. However, little is known about the effect of tolerance, a different mode of survival, on the efficacy of drug combinations for preventing the evolution of resistance. In this work, we monitored Staphylococcus aureus strains evolving in patients under treatment. We detected the rapid emergence of tolerance mutations, followed by the emergence of resistance, despite the combination treatment. Evolution experiments on the clinical strains in vitro revealed a new way by which tolerance promotes the evolution of resistance under combination treatments. Further experiments under different antibiotic classes reveal the generality of the effect. We conclude that tolerance is an important factor to consider in designing combination treatments that prevent the evolution of resistance.

Known Antimicrobials Versus Nortriptyline in Candida albicans: Repositioning an Old Drug for New Targets

Candida albicans has the capacity to develop resistance to commonly used antimicrobials, and to solve this problem, drug repositioning and new drug combinations are being studied. Nortriptyline, a tricyclic antidepressant, was shown to have the capacity to inhibit biofilm and hyphae formation, along with the ability to efficiently kill cells in a mature biofilm. To use nortriptyline as a new antimicrobial, or in combination with known drugs to increase their actions, it is important to characterize in more detail the effects of this drug on the target species. In this study, the Candida albicans GRACE™ collection and a Haplo insufficiency profiling were employed to identify the potential targets of nortriptyline, and to classify, in a parallel screening with amphotericin B, caspofungin, and fluconazole, general multi-drug resistance genes. The results identified mutants that, during biofilm formation and upon treatment of a mature biofilm, are sensitive or tolerant to nortriptyline, or to general drug treatments. Gene ontology analysis recognized the categories of ribosome biogenesis and spliceosome as enriched upon treatment with the tricyclic antidepressant, while mutants in oxidative stress response and general stress response were commonly retrieved upon treatment with any other drug. The data presented suggest that nortriptyline can be considered a “new” antimicrobial drug with large potential for application to in vivo infection models.

Selenite Cystine Agar for Enumeration of Inoculated Salmonella Serovars Recovered from Stressful Conditions During Antimicrobial Validation Studies

Process validation studies often require the inoculation of select foodborne pathogens into targeted foods to determine the lethality of the process or antimicrobial ingredients, and quantitative recovery of surviving inoculum bacteria helps to make those assessments. Such processes introduce various stressors on the inoculated challenge microorganisms whereby traditional selective media are too harsh to enumerate the remaining viable and injured population quantitatively. Innate antibiotic resistance of challenge organisms has often been used to establish simple selective media (i.e., Tryptic Soy Agar/TSA + antibiotics) for recovering inoculated strains, but sometimes antibiotic resistant background microorganisms are higher than desired. Salmonella Thompson 120, Salmonella Heidelberg F5038BG1, Salmonella Hadar MF60404, Salmonella Enteritidis H3527, and Salmonella Typhimurium H3380 were characterized for antibiotic resistance and acid adaptation in Tryptic Soy Broth containing 0%, 0.25%, or 1.0% glucose. Sodium pyruvate was evaluated for recovery after stress but no enhancing effect was observed, possibly because the strains were acid-adapted. Selenite Cystine Broth, traditionally used as a selective enrichment broth, was used as the basis for Selenite Cystine Agar (SCA) in combination with three antibiotics to which our Salmonella are resistant. Serovars of Salmonella, both individually and in mixtures, were enumerated on TSA, SCA, Xylose Lysine Desoxycholate (XLD), and Hektoen Enteric (HE) selective agars (all containing the same antibiotics) after conditions of nutrient starvation, desiccation, acid stress, and thermal stress. The data show that quantitative enumeration of our Salmonella serovars on SCA was not significantly different (p > 0.05) than those achieved on TSA for all tested stress categories. Levels of Salmonella enumerated on XLD and/or HE were significantly different (p < 0.05) than on TSA and SCA and often more than 1-2-log lower, consistent with the inhibition of injured cells. These data confirm that SCA (+ antibiotics) is a suitable selective medium for enumeration of these acid-adapted Salmonella serovars as challenge organisms recovered from various conditions of stress.

Combined X-ray radiotherapy and laser photothermal therapy of melanoma cancer cells using dual-sensitization of platinum nanoparticles

Metal nanostructures are promising agents sensitizing by laser light and X-ray in photothermal therapy (PTT) and radiotherapy (RT) of cancer that improve treatment strategies of cancer. Nanoscale platinum materials are favorable in nanomedicine applications. In this study, platinum nanoparticles (PtNPs) were synthesized and applied for cancer therapy upon 808-nm laser light and X-ray radiation, or their combination. Two power densities of laser (1.0 and 1.5 W cm^-2) and three X-ray doses (2, 4 and 6 Gy) were selected for irradiation of B16/F10 cell line at 24 and 72 h-post treatment. The synthesized PtNPs had a spherical shape with a diameter of 12.2 ± 0.7 nm, and were cytocompatible up to 250 μg mL^-1. A photothermal conversion activity in a concentration-dependent manner at 72 h-post treatment was observed. Also, PtNPs represented cytotoxicity upon X-ray radiation doses of 2, 4, and 6 Gy after 24 h, while, 72-h time passing led to deeper outcomes. Dual radiation of laser light and X-ray into PtNPs considerably improved the treatment via reactive oxygen species (ROS) production. PtNPs can act as a novel dual absorber of laser light and X-ray, a common sensitizer, for treatment of cancer. The results of this study can be considered after further clinical investigations for treatment of tumor models.

In vitro and in vivo Synergistic Effects of Florfenicol and Thiamphenicol in Combination Against Swine Actinobacillus pleuropneumoniae and Pasteurella multocida

Potential synergism between florfenicol (FF) and thiamphenicol (TAP) was investigated for in vitro efficacy against Actinobacillus pleuropneumoniae and/or Pasteurella multocida as well as in vivo efficacy in swine. Among isolates of A. pleuropneumoniae (n = 58) and P. multocida (n = 79) from pigs in Taiwan that were tested, high percentages showed resistance to FF (52 and 53%, respectively) and TAP (57 and 53%, respectively). Checkerboard microdilution assay indicated that synergism [fractional inhibitory concentration index (FICI) ≤ 0.5] was detected in 17% of A. pleuropneumoniae (all serovar 1) and 24% of P. multocida isolates. After reconfirming the strains showing FICI ≤ 0.625 with time kill assay, the synergism increased to around 32% against both bacteria and the number could further increase to 40% against resistant A. pleuropneumoniae and 65% against susceptible P. multocida isolates. A challenge-treatment trial in pigs with P. multocida showed that the FF + TAP dosage at ratios correspondent to their MIC deduction was equally effective to the recommended dosages. Further on the combination, the resistant mutation frequency is very low when A. pleuropneumoniae is grown with FF + TAP and similar to the exposure to sub-inhibitory concentration of FF or TAP alone. The degree of minimum inhibitory concentration (MIC) reduction in FF could reach 75% (1/4 MIC) or more (up to 1/8 MIC for P. multocida, 1/16 for A. pleuropneumoniae) when combined with 1/4 MIC of TAP (or 1/8 for A. pleuropneumoniae). The synergism or FICI ≤ 0.625 of FF with oxytetracycline (47%), doxycycline (69%), and erythromycin (56%) was also evident, and worth further investigation for FF as a central modulator facilitating synergistic effects with these antimicrobials. Taken together, synergistic FF + TAP combination was effective against swine pulmonary isolates of A. pleuropneumoniae and P. multocida both in vitro and in vivo. Thus, this study may offer a potential alternative for the treatment of A. pleuropneumoniae and P. multocida infections and has the potential to greatly reduce drug residues and withdrawal time.

Identification of New MmpL3 Inhibitors by Untargeted and Targeted Mutant Screens Defines MmpL3 Domains with Differential Resistance

The Mycobacterium tuberculosis mycolate flippase MmpL3 has been the proposed target for multiple inhibitors with diverse chemical scaffolds. This diversity in chemical scaffolds has made it difficult to predict compounds that inhibit MmpL3 without whole-genome sequencing of isolated resistant mutants. Here, we describe the identification of four new inhibitors that select for resistance mutations in mmpL3. Using these resistant mutants, we conducted a targeted whole-cell phenotypic screen of 163 novel M. tuberculosis growth inhibitors for differential growth inhibition of wild-type M. tuberculosis compared to the growth of a pool of 24 unique mmpL3 mutants. The screen successfully identified six additional putative MmpL3 inhibitors. The compounds were bactericidal both in vitro and against intracellular M. tuberculosis M. tuberculosis cells treated with these compounds were shown to accumulate trehalose monomycolates, have reduced levels of trehalose dimycolate, and displace an MmpL3-specific probe, supporting MmpL3 as the target. The inhibitors were mycobacterium specific, with several also showing activity against the nontuberculous mycobacterial species M. abscessus Cluster analysis of cross-resistance profiles generated by dose-response experiments for each combination of 13 MmpL3 inhibitors against each of the 24 mmpL3 mutants defined two clades of inhibitors and two clades of mmpL3 mutants. Pairwise combination studies of the inhibitors revealed interactions that were specific to the clades identified in the cross-resistance profiling. Additionally, modeling of resistance-conferring substitutions to the MmpL3 crystal structure revealed clade-specific localization of the residues to specific domains of MmpL3, with the clades showing differential resistance. Several compounds exhibited high solubility and stability in microsomes and low cytotoxicity in macrophages, supporting their further development. The combined study of multiple mutants and novel compounds provides new insights into structure-function interactions of MmpL3 and small-molecule inhibitors.

Emodin Reduces the Activity of (1,3)-β-D-glucan Synthase from Candida albicans and Does Not Interact with Caspofungin

Candidiasis is the most common opportunistic yeast infection, with Candida albicans as a paramount causative species. (1,3)-β-D-glucan is one of the three main targets of clinically available antifungal agents used to treat Candida infections. It is one of the most abundant fungal cell wall components. Echinocandins represent the newest class of antifungals affecting cell wall biosynthesis through non-competitive inhibition of (1,3)-β-D-glucan synthase. Therefore, treatment with echinocandins causes defects in fungal cell integrity. In the present study, similar activity of emodin (6-methyl-1,3,8-trihydroxyanthraquinone) has been revealed. Many reports have already shown the antifungal potential of this pleiotropic molecule, including its activity against C. albicans. The aim of this report was to evaluate the activity of emodin towards a new molecular target, i.e. (1,3)-β-D-glucan synthase isolated from Candida cells. Moreover, given the identical mechanism of the activity of both molecules, interaction of emodin with caspofungin was determined. The study revealed that emodin reduced (1,3)-β-D-glucan synthase activity and increased cell wall damage, which was evidenced by both a sorbitol protection assay and an aniline blue staining assay. Furthermore, the synergy testing method showed mainly independence of the action of both tested antifungal agents, i.e. emodin and caspofungin used in combination.

Noise-precision tradeoff in predicting combinations of mutations and drugs

Many biological problems involve the response to multiple perturbations. Examples include response to combinations of many drugs, and the effects of combinations of many mutations. Such problems have an exponentially large space of combinations, which makes it infeasible to cover the entire space experimentally. To overcome this problem, several formulae that predict the effect of drug combinations or fitness landscape values have been proposed. These formulae use the effects of single perturbations and pairs of perturbations to predict triplets and higher order combinations. Interestingly, different formulae perform best on different datasets. Here we use Pareto optimality theory to quantitatively explain why no formula is optimal for all datasets, due to an inherent bias-variance (noise-precision) tradeoff. We calculate the Pareto front of log-linear formulae and find that the optimal formula depends on properties of the dataset: the typical interaction strength and the experimental noise. This study provides an approach to choose a suitable prediction formula for a given dataset, in order to best overcome the combinatorial explosion problem.

Sometimes a combination of drugs works much better than each drug alone. Finding such drug cocktails is a pressing challenge in order to combat drug resistance and to improve drug effects. However, it is impossible to test all combinations of multiple drug experimentally. Therefore, researchers are looking for computational rather than experimental approaches to overcome this problem. One approach is to measure the effect of few drugs and plug it into a formula that predicts the effect of many drugs together. Existing prediction formulae typically perform best on the dataset that they were developed on, but less well on other datasets. Here we explain this observation and give a guide for the choice of an optimal prediction formula for a given dataset. The optimal formula depends on two main properties of the dataset: 1) The interaction strength between the drugs and 2) The experimental noise in the data. This study may help researchers discover effective combinations of multiple drugs and multiple perturbations in general.

Pairwise combinations of chemical compounds that delay yeast chronological aging through different signaling pathways display synergistic effects on the extent of aging delay

We have recently discovered six plant extracts that delay yeast chronological aging. Most of them affect different nodes, edges and modules of an evolutionarily conserved network of longevity regulation that integrates certain signaling pathways and protein kinases; this network is also under control of such aging-delaying chemical compounds as spermidine and resveratrol. We have previously shown that, if a strain carrying an aging-delaying single-gene mutation affecting a certain node, edge or module of the network is exposed to some of the six plant extracts, the mutation and the plant extract enhance aging-delaying efficiencies of each other so that their combination has a synergistic effect on the extent of aging delay. We therefore hypothesized that a pairwise combination of two aging-delaying plant extracts or a combination of one of these plant extracts and spermidine or resveratrol may have a synergistic effect on the extent of aging delay only if each component of this combination targets a different element of the network. To test our hypothesis, we assessed longevity-extending efficiencies of all possible pairwise combinations of the six plant extracts or of one of them and spermidine or resveratrol in chronologically aging yeast. In support of our hypothesis, we show that only pairwise combinations of naturally-occurring chemical compounds that slow aging through different nodes, edges and modules of the network delay aging in a synergistic manner.