Novel synergistic interactions between monolaurin, a mono-acyl glycerol and ß lactam antibiotics against Staphylococcus aureus: an in vitro study.

BACKGROUND: A major worldwide health issue is the rising frequency of resistance of bacteria.Drug combinations are a winning strategy in fighting resistant bacteria and might help in protecting the existing drugs.Monolaurin is natural compound extracted from coconut oil and has a promising antimicrobial activity against Staphylococcus.aureus. This study aims to examine the efficacy of monolaurin both individually and in combination with ß-lactam antibiotics against Staphylococcus aureus isolates. METHODS: Agar dilution method was used for determination of minimum inhibitory concentration (MIC) of monolaurin against S.aureus isolates. Scanning electron microscope (SEM) was used to detect morphological changes in S.aureus after treatment with monolaurin. Conventional and Real-time Polymerase chain reaction (RT-PCR) were performed to detect of beta-lactamase (blaZ) gene and its expressional levels after monolaurin treatment. Combination therapy of monolaurin and antibiotics was assessed through fractional inhibitory concentration and time-kill method. RESULTS: The antibacterial activity of monolaurin was assessed on 115 S.aureus isolates, the MIC of monolaurin were 250 to 2000 µg/ml. SEM showed cell elongation and swelling in the outer membrane of S.aureus in the prescence of 1xMIC of monolaurin. blaZ gene was found in 73.9% of S.aureus isolates. RT-PCR shows a significant decrease in of blaZ gene expression at 250 and 500 µg/ml of monolaurin. Synergistic effects were detected through FIC method and time killing curve. Combination therapy established a significant reduction on the MIC value. The collective findings from the antibiotic combinations with monolaurin indicated synergism rates ranging from 83.3% to 100%.In time-kill studies, combination of monolaurin and ß-lactam antibiotics produced a synergistic effect. CONCLUSION: This study showed that monolaurin may be a natural antibacterial agent against S. aureus, and may be an outstanding modulator of ß-lactam drugs. The concurrent application of monolaurin and ß-lactam antibiotics, exhibiting synergistic effects against S. aureus in vitro, holds promise as potential candidates for the development of combination therapies that target particularly, patients with bacterial infections that are nearly incurable.

Efficacy of delafloxacin alone and incombination with cefotaxime againstcefotaxime non-susceptible invasive isolatesof Streptococcus pneumoniae / Eficacia de delafloxacino solo y en combinación con cefotaxima frente a aislados invasivos de Streptococcus pneumoniae no sensible acefotaxima

Objectives. We assessed the in vitro activity of delafloxacin and the synergy between cefotaxime and delafloxacin among cefotaxime non-susceptible invasive isolates of Streptococcus pneumoniae (CNSSP). Material and methods. A total of 30 CNSSP (cefotaxime MIC > 0.5 mg/L) were studied. Serotyping was performed by the Pneumotest-Latex and Quellung reaction. Minimum inhibitory concentrations (MICs) of delafloxacin, levofloxacin, penicillin, cefotaxime, erythromycin and vancomycin were determined by gradient diffusion strips (GDS). Synergistic activity of delafloxacin plus cefotaxime against clinical S. pneumoniae isolates was evaluated by the GDS cross method. Results. Delafloxacin showed a higher pneumococcal activity than its comparator levofloxacin (MIC50, 0.004 versus 0.75 mg/L and MIC90, 0.047 versus >32 mg/L). Resistance to delafloxacin was identified in 7/30 (23.3%) isolates, belonging to serotypes 14 and 9V. Synergy between delafloxacin and cefotaxime was detected in 2 strains (serotypes 19A and 9V). Antagonism was not observed. Addition of delafloxacin increased the activity of cefotaxime in all isolates. Delafloxacin susceptibility was restored in 5/7 (71.4%) strains. Conclusions. CNSSP showed a susceptibility to delafloxacin of 76.7%. Synergistic interactions between delafloxacin and cefotaxime were observed in vitro among CNSSP by GDS cross method. (AU)

Objetivos. Evaluamos la actividad in vitro de delafloxacino y la sinergia entre cefotaxima y delafloxacino entre aislados invasivos de Streptococcus pneumoniae no sensibles a cefotaxima (SPNSC). Material y métodos. Se estudiaron un total de 30 SPNSC (CIM de cefotaxima > 0,5 mg/L). El serotipado se realizó mediante la reacción Pneumotest-Latex y Quellung. Las concentraciones mínimas inhibitorias (CMI) de delafloxacino, levofloxacino, penicilina, cefotaxima, eritromicina y vancomicina se determinaron mediante tiras de difusión en gradiente (GDS). La actividad sinérgica de delafloxacino y cefotaxima frente aislados clínicos de S. pneumoniae se evaluó mediante el método cruzado GDS. Resultados. Delafloxacino mostró una mayor actividad neumocócica que su comparador levofloxacino (CIM50, 0,004 versus 0,75 mg/L y MIC90, 0,047 versus > 32 mg/L). Se identificó resistencia a delafloxacino en 7/30 (23,3%) aislados, pertenecientes a los serotipos 14 y 9V. Se detectó sinergia entre delafloxacino y cefotaxima en 2 cepas (serotipos 19A y 9V). No se observó antagonismo. La adición de delafloxacino aumentó la actividad de cefotaxima en todos los aislados. La sensibilidad a delafloxacino se restableció en 5/7 (71,4%) cepas. Conclusiones. SPNSC mostraron una susceptibilidad a delafloxacino del 76,7%. Se observaron interacciones sinérgicas in vitro entre delafloxacino y cefotaxima entre SPNSC mediante el método cruzado GDS. (AU)

Essential Oil Compounds in Combination with Conventional Antibiotics for Dermatology.

Antimicrobial resistance has emerged as a significant threat to public health, prompting novel combinations comprising of natural sources such as essential oil compounds with conventional antibiotics. This study aimed to determine the possible interactions between six essential oil compounds with eight antibiotics/antifungals against six pathogens (Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, Acinetobacter baumannii, Cutibacterium acnes, and Candida albicans) commonly implicated in skin infections. The minimum inhibitory concentrations (MICs) for the antibiotics and essential oil compounds were evaluated singularly and in combination using the broth microdilution assay. The fractional inhibitory concentrations (FIC) were calculated to determine the interactive profile of the combinations. The synergistic interactions (FIC ≤ 0.5) were further analysed at varying ratios and depicted on isobolograms. The toxicity of the synergistic combinations was determined using the brine shrimp lethality assay. Eight synergistic interactions were identified against the selected Gram-positive and P. aeruginosa pathogens, and the combinations also demonstrated a reduced toxicity. The combination of amoxicillin and eugenol demonstrated the lowest toxicity (LC50 = 1081 µg/mL) and the highest selectivity index (14.41) when in a 70:30 ratio. This study provides insight into the in vitro antimicrobial interactions of essential oil compounds and conventional antibiotics that can form a basis for newer therapies.

Synergistic Augmentation of Beta-Lactams: Exploring Quinoline-Derived Amphipathic Small Molecules as Antimicrobial Potentiators against Methicillin-Resistant Staphylococcus aureus.

The escalation of bacterial resistance against existing therapeutic antimicrobials has reached a critical peak, leading to the rapid emergence of multidrug-resistant strains. Stringent pathways in novel drug discovery hinder our progress in this survival race. A promising approach to combat emerging antibiotic resistance involves enhancing conventional ineffective antimicrobials using low-toxicity small molecule adjuvants. Recent research interest lies in weak membrane-perturbing agents with unique cyclic hydrophobic components, addressing a significant gap in antimicrobial drug exploration. Our study demonstrates that quinoline-based amphipathic small molecules, SG-B-52 and SG-B-22, significantly reduce MICs of selected beta-lactam antibiotics (ampicillin and amoxicillin) against lethal methicillin-resistant Staphylococcus aureus (MRSA). Mechanistically, membrane perturbation, depolarization, and ROS generation drive cellular lysis and death. These molecules display minimal in vitro and in vivo toxicity, showcased through hemolysis assays, cell cytotoxicity analysis, and studies on albino Wistar rats. SG-B-52 exhibits impressive biofilm-clearing abilities against MRSA biofilms, proposing a strategy to enhance beta-lactam antibiosis and encouraging the development of potent antimicrobial potentiators.

High-content analysis identified synergistic drug interactions between INK128, an mTOR inhibitor, and HDAC inhibitors in a non-small cell lung cancer cell line.

BACKGROUND: The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations. METHODS: To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses. RESULTS: By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails. CONCLUSIONS: In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.

Combination Treatment with EGFR Inhibitor and Doxorubicin Synergistically Inhibits Proliferation of MCF-7 Cells and MDA-MB-231 Triple-Negative Breast Cancer Cells In Vitro.

The role of the epidermal growth factor receptor (EGFR) in tumor progression and survival is often underplayed. Its expression and/or dysregulation is associated with disease advancement and poor patient outcome as well as drug resistance in breast cancer. EGFR is often overexpressed in breast cancer and particularly triple-negative breast cancer (TNBC), which currently lacks molecular targets. We examined the synergistic potential of an EGFR inhibitor (EGFRi) in combination with doxorubicin (Dox) in estrogen-positive (ER+) MCF-7 and MDA-MB-231 TNBC cell lines. The exposure of MDA-MB-231 and MCF-7 to EGFRi produced an IC50s of 6.03 µM and 3.96 µM, respectively. Dox induced MDA-MB-231 (IC50 9.67 µM) and MCF-7 (IC50 1.4 µM) cytotoxicity. Combinations of EGFRi-Dox significantly reduced the IC50 in MCF-7 (0.46 µM) and MBA-MB 231 (0.01 µM). Synergistic drug interactions in both cell lines were confirmed using the Bliss independence model. Pro-apoptotic Caspase-3/7 activation occurred in MCF-7 at 0.1-10 µM of EGFRi and Dox single treatments, whilst 1 µM Dox yielded a more potent effect on MDA-MB-231. EGFRi and Dox individually and in combination downregulated the EGFR gene expression in MCF-7 and MDA-MB-231 (p < 0.001). This study demonstrates EGFRi's potential for eliciting synergistic interactions with Dox, causing enhanced growth inhibition, apoptosis induction, and downregulation of EGFR in both cell lines.

Synergistic interaction of polymyxin B with carvacrol: antimicrobial strategy against polymyxin-resistant Klebsiella pneumoniae.

Objective: The antimicrobial activities of the synergistic combination of carvacrol and polymyxin B against polymyxin-resistant Klebsiella pneumoniae were evaluated. Methods: The methods employed checkerboard assays to investigate synergism, biofilm inhibition assessment and membrane integrity assay. In addition, the study included in vivo evaluation using a mouse infection model. Results: The checkerboard method evaluated 48 combinations, with 23 indicating synergistic action. Among these, carvacrol 10 mg/kg plus polymyxin B 2 mg/kg exhibited in vivo antimicrobial activity in a mouse model of infection, resulting in increased survival and a significant decrease in bacterial load in the blood. Conclusion: Polymyxin in synergy with carvacrol represents a promising alternative to be explored in the development of new antimicrobials.

In this study, we wanted to find a new way to fight a bacteria called Klebsiella pneumoniae, which is not easily killed by medication. We mixed two drugs, carvacrol and polymyxin B, to see if they would work together to fight the bacteria. We found that the mixed treatment helped to kill the bacteria. We also tried this mixed treatment in sick mice, and they got better. Our study shows that this mixed treatment might be a new way to fight bacteria that are hard to kill with regular drugs. Next, we hope to learn more about how it works.

The synergic and addictive activity of biogenic silver nanoparticle associated with meropenem against carbapenem-resistant Acinetobacter baumannii.

AIMS: Antibiotic management of infections caused by Acinetobacter baumannii often fails due to antibiotic resistance (especially to carbapenems) and biofilm-forming strains. Thus, the objective here was to evaluate in vitro the antibacterial and antibiofilm activity of biogenic silver nanoparticle (Bio-AgNP) combined with meropenem, against multidrug-resistant isolates of A. baumannii. METHODS AND RESULTS: In this study, A. baumannii ATCC® 19606™ and four carbapenem-resistant A. baumannii (Ab) strains were used. The antibacterial activity of Bio-AgNP and meropenem was evaluated through broth microdilution. The effect of the Bio-AgNP association with meropenem was determined by the checkboard method. Also, the time-kill assay and the integrity of the bacterial cell membrane were evaluated. Furthermore, the antibiofilm activity of Bio-AgNP and meropenem alone and in combination was determined. Bio-AgNP has antibacterial activity with minimum inhibitory concentration (MIC) and minimum bactericidal concentration ranging from 0.46 to 1.87 µg ml-1. The combination of Bio-AgNP and meropenem showed a synergistic and additive effect against Ab strains, and Bio-AgNP was able to reduce the MIC of meropenem from 4- to 8-fold. Considering the time-kill of the cell, meropenem and Bio-AgNP when used in combination reduced bacterial load to undetectable levels within 10 min to 24 h after treatment. Protein leakage was observed in all treatments evaluated. When combined, meropenem/Bio-AgNP presents biofilm inhibition for Ab2 isolate and ATCC® 19606™, with 21% and 19%, and disrupts the biofilm from 22% to 50%, respectively. The increase in nonviable cells in the biofilm can be observed after treatment with Bio-AgNP and meropenem in carbapenem-resistant A. baumannii strains. CONCLUSIONS: The combination of Bio-AgNP with meropenem can be a therapeutic option in the treatment of infections caused by carbapenem-resistant A. baumannii.

Combinatorial effects between aromatic plant compounds and chlorhexidine digluconate against canine otitis-related Staphylococcus spp.

The increasing prevalence of antimicrobial resistance among bacterial pathogens necessitates novel treatment strategies, particularly in veterinary medicine where otitis in dogs is very common in small animals' clinical routines. Considering this challenge, this study explores the efficacy of aromatic plant compounds (APC), including eugenol (EUG), trans-cinnamaldehyde (TC), and geraniol (GER), and their synergistic potential when combined with the antiseptic agent chlorhexidine (CLX), offering insight into alternative therapeutic approaches. The disk diffusion assay revealed differential sensitivity of Staphylococcus spp. strains to the tested compounds, with EUG and GER showing moderate inhibition zones and TC displaying considerably larger inhibition zones. Further analysis through MIC and MBC determinations suggested that EUG required the highest concentrations to inhibit and kill the bacteria, whereas TC and GER were effective at lower concentrations. Combined with CLX, all three plant-derived compounds demonstrated a significant enhancement of antibacterial activity, indicated by reduced MIC values and a predominantly synergistic interaction across the strains tested. GER was the most potent in combination with CLX, presenting the lowest mean FICi values and the highest fold reductions in MIC. This study emphasizes the APC's potential as an adjunct to conventional antimicrobial agents like CLX. The marked synergy observed, especially with GER, suggests that such combinations could be promising alternatives in managing bacterial otitis in dogs, potentially mitigating the impact of antibiotic resistance.

Within-species variability of antibiotic interactions in Gram-negative bacteria.

Treatments with antibiotic combinations are becoming increasingly important even though the supposed clinical benefits of combinations are, in many cases, unclear. Here, we systematically examined how several clinically used antibiotics interact and affect the antimicrobial efficacy against five especially problematic Gram-negative pathogens. A total of 232 bacterial isolates were tested against different pairwise antibiotic combinations spanning five classes, and the ability of all combinations in inhibiting growth was quantified. Descriptive statistics, principal component analysis (PCA), and Spearman's rank correlation matrix were used to determine the correlations between the different combinations on interaction outcome. Several important conclusions can be drawn from the 696 examined interactions. Firstly, within a species, the interactions are in general conserved but can be isolate-specific for a given antibiotic combination and can range from antagonistic to synergistic. Secondly, additive and antagonistic interactions are the most common observed across species and antibiotics, with 87.1% of isolate-antibiotic combinations being additive, 11.6% antagonistic, and only 0.3% showing synergy. These findings suggest that to achieve the highest precision and efficacy of combination therapy, not only isolate-specific interaction profiling ought to be routinely performed, in particular to avoid using drug combinations that show antagonistic interaction and an expected associated reduction in efficacy, but also discovering rare and potentially valuable synergistic interactions.IMPORTANCEAntibiotic combinations are often used to treat bacterial infections, which aim to increase treatment efficacy and reduce resistance evolution. Typically, it is assumed that one specific antibiotic combination has the same effect on different isolates of the same species, i.e., the interaction is conserved. Here, we tested this idea by examining how several clinically used antibiotics interact and affect the antimicrobial efficacy against several bacterial pathogens. Our results show that, even though within a species the interactions are often conserved, there are also isolate-specific differences for a given antibiotic combination that can range from antagonistic to synergistic. These findings suggest that isolate-specific interaction profiling ought to be performed in clinical microbiology routine to avoid using antagonistic drug combinations that might reduce treatment efficacy.

Comparison of in vitro synergy between polymyxin B or colistin in combination with 16 antimicrobial agents against multidrug-resistant Acinetobacter baumannii isolates.

PURPOSES: This study determined the synergy of polymyxin B (POLB) and colistin (COL) with 16 other tested antimicrobial agents in the inhibition of multidrug-resistant Acinetobacter baumannii (MDR-AB). METHODS: We used chequerboard assays to determine synergy between the drugs against 50 clinical MDR-AB from a tertiary hospital in the Zhejiang province in 2019, classifying combinations as either antagonistic, independent, additive, or synergistic. The efficacy of hit combinations which showed highest synergistic rate were confirmed using time-kill assays. RESULTS: Both POLB and COL displayed similar bactericidal effects when used in combination with these 16 tested drugs. Antagonism was only observed for a few strains (2%) exposed to a combination of POLB and cefoperazone/sulbactam (CSL). A higher percentage of synergistic combinations with POLB and COL were observed with rifabutin (RFB; 90%/96%), rifampicin (RIF; 60%/78%) and rifapentine (RFP; 56%/76%). Time-kill assays also confirmed the synergistic effect of POLB and rifamycin class combinations. 1/2 MIC rifamycin exposure can achieve bacterial clearance when combined with 1/2 MIC POLB or COL. CONCLUSION: Nearly no antagonism was observed when combining polymyxins with other drugs by both chequerboard and time-kill assays, suggesting that polymyxins may be effective in combination therapy. The combinations of POLB/COL with RFB, RIF, and RFP displayed neat synergy, with RFB showing the greatest effect.

In-Silico Identification of Novel Pharmacological Synergisms: The Trabectedin Case.

The in-silico strategy of identifying novel uses for already existing drugs, known as drug repositioning, has enhanced drug discovery. Previous studies have shown a positive correlation between expression changes induced by the anticancer agent trabectedin and those caused by irinotecan, a topoisomerase I inhibitor. Leveraging the availability of transcriptional datasets, we developed a general in-silico drug-repositioning approach that we applied to investigate novel trabectedin synergisms. We set a workflow allowing the identification of genes selectively modulated by a drug and possible novel drug interactions. To show its effectiveness, we selected trabectedin as a case-study drug. We retrieved eight transcriptional cancer datasets including controls and samples treated with trabectedin or its analog lurbinectedin. We compared gene signature associated with each dataset to the 476,251 signatures from the Connectivity Map database. The most significant connections referred to mitomycin-c, topoisomerase II inhibitors, a PKC inhibitor, a Chk1 inhibitor, an antifungal agent, and an antagonist of the glutamate receptor. Genes coherently modulated by the drugs were involved in cell cycle, PPARalpha, and Rho GTPases pathways. Our in-silico approach for drug synergism identification showed that trabectedin modulates specific pathways that are shared with other drugs, suggesting possible synergisms.

Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy.

Breast cancer is one of the most common types of cancer in females. While drug combinations have shown potential in breast cancer treatments, identifying new effective drug pairs is challenging due to the vast number of possible combinations among available compounds. Efforts have been made to accelerate the process with in silico predictions. Here, we developed a Boolean model of signaling pathways in breast cancer. The model was tailored to represent five breast cancer cell lines by integrating information about cell-line specific mutations, gene expression, and drug treatments. The models reproduced cell-line specific protein activities and drug-response behaviors in agreement with experimental data. Next, we proposed a calculation of protein synergy scores (PSSs), determining the effect of drug combinations on individual proteins' activities. The PSSs of selected proteins were used to investigate the synergistic effects of 150 drug combinations across five cancer cell lines. The comparison of the highest single agent (HSA) synergy scores between experiments and model predictions from the MDA-MB-231 cell line achieved the highest Pearson's correlation coefficient of 0.58 with a great balance among the classification metrics (AUC = 0.74, sensitivity = 0.63, and specificity = 0.64). Finally, we clustered drug pairs into groups based on the selected PSSs to gain further insights into the mechanisms underlying the observed synergistic effects of drug pairs. Clustering analysis allowed us to identify distinct patterns in the protein activities that correspond to five different modes of synergy: 1) synergistic activation of FADD and BID (extrinsic apoptosis pathway), 2) synergistic inhibition of BCL2 (intrinsic apoptosis pathway), 3) synergistic inhibition of MTORC1, 4) synergistic inhibition of ESR1, and 5) synergistic inhibition of CYCLIN D. Our approach offers a mechanistic understanding of the efficacy of drug combinations and provides direction for selecting potential drug pairs worthy of further laboratory investigation.

The combined anti-tumor effects of 5-fluorouracil and neurokinin receptor inhibitor, aprepitant, against colorectal cancer: In vitro and in vivo study.

BACKGROUND: Colorectal cancer (CRC) is one of the world's largest health concerns with growing global incidence and mortality. The potential value of the neurokinin-1 receptor as a therapeutic target has been reported in several tumor types, including CRC. Here we examined the potential anti-tumor effects of a clinically approved neurokinin-1 receptor antagonist, aprepitant, alone and its combination with 5-Fluorouracil (5-FU) as a first choice CRC chemotherapeutic drug, in both in vitro and in vivo models of CRC. METHODS: MTT assay was employed for assessing cell proliferation. mRNA expression levels were determined by quantitative real-time PCR (qRT-PCR). Flow cytometric analysis of apoptosis was performed using an Annexin-V/propidium iodide assay kit. We finally conducted an in vivo experiment in a mouse model of CRC to confirm the in vitro antiproliferative activity of aprepitant and 5-FU. RESULTS: We found that aprepitant and 5-FU significantly reduced CRC cell viability. The combination of drugs exhibited potent synergistic growth inhibitory effects on CRC cells. Moreover, aprepitant and 5-FU induced apoptosis and altered the levels of apoptotic genes (up-regulation of Bax, and p53 along with downregulation of Bcl-2). Importantly, the aprepitant and 5-FU combination showed a more pronounced impact on apoptosis and associated genes than either of the agents alone. Furthermore, aprepitant reduced tumor growth in vivo and led to significantly longer survival time, and this effect was more prominent when using the aprepitant and 5-FU combination. CONCLUSIONS: Collectively, combinatory treatment with aprepitant and 5-FU potentially exerts synergistic growth inhibition and apoptosis induction in CRC, deserving further consideration as a novel strategy for CRC patients.

Interactions of plumbagin with five common antibiotics against Staphylococcus aureus in vitro.

Staphylococcus aureus is the main culprit, causing a variety of severe clinical infections. At the same time, clinics are also facing the severe situation of antibiotic resistance. Therefore, effective strategies to address this problem may include expanding the antimicrobial spectrum by exploring alternative sources of drugs or delaying the development of antibiotic resistance through combination therapy so that existing antibiotics can continue to be used. Plumbagin (PLU) is a phytochemical that exhibits antibacterial activity. In the present study, we investigated the in vitro antibacterial activity of PLU. We selected five antibiotics with different mechanisms and inhibitory activities against S. aureus to explore their interaction with the combination of PLU. The interaction of combinations was evaluated by the Bliss independent model and visualized through response surface analysis. PLU exhibited potent antibacterial activity, with half maximal inhibitory concentration (IC50) and minimum inhibitory concentration (MIC) values against S. aureus of 1.73 µg/mL and 4 µg/mL, respectively. Synergism was observed when PLU was combined with nitrofurantoin (NIT), ciprofloxacin (CPR), mecillinam (MEC), and chloramphenicol (CHL). The indifference of the trimethoprim (TMP)-PLU pairing was demonstrated across the entire dose-response matrix, but significant synergy was observed within a specific dose region. In addition, no antagonistic interactions were indicated. Overall, PLU is not only a promising antimicrobial agent but also has the potential to enhance the growth-inhibitory activity of some antibiotics against S. aureus, and the use of the interaction landscape, along with the dose-response matrix, for analyzing and quantifying combination results represents an improved approach to comprehending antibacterial combinations.

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs.

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.

Dihydroartemisinin Enhances the Therapeutic Efficacy of BH3 Mimetic Inhibitor in Acute Lymphoblastic Leukemia Cells via Inhibition of Mcl-1.

INTRODUCTION: Up-regulation of the anti-apoptotic proteins such as Mcl-1 is associated with the primary and secondary resistance of tumor cells to ABT-737 Bcl-2 inhibitor. The combined treatment of Bcl-2 inhibitors with Mcl-1 inhibitors has been proposed as an attractive therapeutic strategy to overcome this drug resistance. Here, we investigated the effect of dihydroartemisinin on Mcl-1 expression and sensitization of T-ALL cells to ABT-737. METHODS: The cell growth and survival were tested by the cell proliferation and MTT assays, respectively. The mRNA levels of Bcl-2, Mcl-1, Bax and P21 were examined by qRT-PCR. Apoptosis were detected by Hoechst 33342 staining and caspase-3 activity assay. RESULTS: Our data showed that combination treatment with dihydroartemisinin and ABT-737 caused a significant decrease in the IC50 value and synergistically reduced the cell survival compared with dihydroartemisinin or ABT-737 alone. ABT-737 enhanced the Mcl-1 mRNA expression. Dihydroartemisinin also down-regulated the expression of Bcl-2 and Mcl-1 and enhanced the P21 and Bax expression. Moreover, dihydroartemisinin enhanced the apoptosis induced by ABT-737 in MOLT-4 and MOLT-17 cell lines. CONCLUSION: In conclusion, dihydroartemisinin demonstrates anti-tumor activities in human ALL cells via inhibition of cell survival and growth. Dihydroartemisinin augments the apoptotic effect of ABT-737 by inhibiting the expression of Mcl-1.

The Effects of ABT-199 and Dihydroartemisinin Combination on Cell Growth and Apoptosis in Human U937 and KG-1 Cancer Cells.

INTRODUCTION: Change in the balance of Bcl-2 family proteins is one of the main reasons for resistance of tumor cells to ABT-199. In this study, the effect of dihydroartemisinin on cell growth, apoptosis and sensitivity of the AML cells to ABT-199 was investigated. METHODS: Cell proliferation and survival were assessed by trypan blue staining and MTT assay, respectively. Cell apoptosis was measured by Hoechst 33342 staining and caspase-3 activity assay. The expression levels of Bcl-2, Mcl-1 and Bax mRNA were tested by qRT-PCR. RESULTS: Our data showed that combination therapy significantly reduced the IC50 value and synergistically decreased the AML cell survival and growth compared with dihydroartemisinin or ABT-199 alone. Treatment with each of ABT-199 or dihydroartemisinin alone clearly enhanced the Bax mRNA expression and inhibited the expression of Mcl-1 and Bcl-2 mRNA. Inhibition of Mcl-1 mRNA by dihydroartemisinin was associated with enhancement of apoptosis induced by ABT-199 in AML cells. CONCLUSION: In conclusion, dihydroartemisinin not only triggers the intrinsic pathway of apoptosis, but also can increase the sensitivity of the AML cells to ABT-199 via suppression of Mcl-1 expression.

Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.

Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset. Using a new drug similarity metric, we augmented the synergy data by substituting a compound in a drug combination instance with another molecule that exhibits highly similar pharmacological effects. Using this protocol, we were able to upscale the AZ-DREAM Challenges dataset from 8798 to 6,016,697 drug combinations. Comprehensive performance evaluations show that ML models trained on the augmented data consistently achieve higher accuracy than those trained solely on the original dataset. Our data augmentation protocol provides a systematic and unbiased approach to generating more diverse and larger-scale drug combination datasets, enabling the development of more precise and effective ML models. The protocol presented in this study could serve as a foundation for future research aimed at discovering novel and effective drug combinations for cancer treatment.

Unlocking the therapeutic potential of drug combinations through synergy prediction using graph transformer networks.

Drug combinations are frequently used to treat cancer to reduce side effects and increase efficacy. The experimental discovery of drug combination synergy is time-consuming and expensive for large datasets. Therefore, an efficient and reliable computational approach is required to investigate these drug combinations. Advancements in deep learning can handle large datasets with various biological problems. In this study, we developed a SynergyGTN model based on the Graph Transformer Network to predict the synergistic drug combinations against an untreated cancer cell line expression profile. We represent the drug via a graph, with each node and edge of the graph containing nine types of atomic feature vectors and four bonds features, respectively. The cell lines represent based on their gene expression profiles. The drug graph was passed through the GTN layers to extract a generalized feature map for each drug pairs. The drug pair extracted features and cell-line gene expression profiles were concatenated and subsequently subjected to processing through multiple densely connected layers. SynergyGTN outperformed the state-of-the-art methods, with a receiver operating characteristic area under the curve improvement of 5% on the 5-fold cross-validation. The accuracy of SynergyGTN was further verified through three types of cross-validation tests strategies namely leave-drug-out, leave-combination-out, and leave-tissue-out, resulting in improvement in accuracy of 8%, 1%, and 2%, respectively. The Astrazeneca Dream dataset was utilized as an independent dataset to validate and assess the generalizability of the proposed method, resulting in an improvement in balanced accuracy of 13%. In conclusion, SynergyGTN is a reliable and efficient computational approach for predicting drug combination synergy in cancer treatment. Finally, we developed a web server tool to facilitate the pharmaceutical industry and researchers, as available at: http://nsclbio.jbnu.ac.kr/tools/SynergyGTN/.