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Melanoma has a low incidence, accounting for less than 5% of skin cancers; however, it is the most lethal cancer, primarily because of its high potential for metastasis and resistance to different treatments. Natural products can sensitize melanoma to chemotherapy and overcome drug resistance. Previous studies have reported Cedrus atlantica extract has various pharmacological benefits such as anti-inflammatory, antioxidant, antibacterial, and analgesic properties. This study aimed to explore the effects of C. atlantica extract (CAt) against melanoma in vitro and in vivo. The effects of CAt on B16F10 cell viability, proliferation, migration, invasion, and apoptosis were detected using MTT, colony formation, wound-healing, Boyden chamber, and TUNEL assays. Semi-quantitative RT-PCR and western blotting were used to measure mRNA and protein expression, respectively. Results revealed that CAt selectively decreased the viability of B16F10 cells and inhibited colony formation in a dose-dependent manner. CAt reduces cell migration and invasion by regulating epithelial-mesenchymal transition-associated proteins (Snail, E-cadherin, and vimentin). Moreover, CAt enhanced the Bax/Bcl-2 ratio and the expression of cleaved-caspase-9, caspase-3, and PARP1, resulting in the activation of mitochondria-mediated apoptosis. In an in vivo study, CAt significantly inhibited tumor growth and prolonged the lifespan of mice at a well-tolerated dose. Importantly, the combination of CAt and 5-fluorouracil (5-FU) exhibited synergistic growth suppression and attenuated the development of 5-FU resistance. Overall, the findings suggest that CAt holds promise as a potential drug or adjuvant to improve melanoma treatment. [ABSTRACT FROM AUTHOR]

Candida albicans is a common opportunistic pathogen, causing infections ranging from superficial to bloodstream infections. The limited antifungal options and rising drug resistance challenge clinical treatment. We screened 98 essential oils and identified 48 with antifungal activity against Candida albicans at 1% concentration, determining their minimum inhibitory concentrations (MIC). Of these, 14 maintained fungicidal activity at lower concentrations (0.25% and 0.125%). 5 essential oils (Cinnamon, Satureja montana, Palmarosa, Lemon eucalyptus, and Honey myrtle) showed the highest inhibitory effects on stationary-phase Candida albicans and inhibited hyphae elongation. Synergistic effects were observed when combining Palmarosa with amphotericin B (AmB) against growing-phase Candida albicans, while Cinnamon and Satureja montana with AmB showed superior efficacy against stationary-phase infections. We identified the active components of 5 essential oils using gas chromatography-mass spectrometry (GC-MS) and found the following main constituents: Cinnamon primarily contains benzyl benzoate and eugenol, Satureja montana is dominated by carvacrol and cymene, Palmarosa features geraniol and geranyl acetate, Lemon eucalyptus includes dl-Isopulegol and citronellal, and Honey myrtle is characterized by citral and neral. Our results may aid in developing more effective antifungal treatments. [ABSTRACT FROM AUTHOR]

Loss of function screens using shRNA (short hairpin RNA) and CRISPR (clustered regularly interspaced short palindromic repeats) are routinely used to identify genes that modulate responses of tumor cells to anti-cancer drugs. Here, by integrating GSEA (Gene Set Enrichment Analysis) and CMAP (Connectivity Map) analyses of multiple published shRNA screens, we identified a core set of pathways that affect responses to multiple drugs with diverse mechanisms of action. This suggests that these pathways represent "weak points" or "Achilles heels", whose mild disturbance should make cancer cells vulnerable to a variety of treatments. These "weak points" include proteasome, protein synthesis, RNA splicing, RNA synthesis, cell cycle, Akt-mTOR, and tight junction-related pathways. Therefore, inhibitors of these pathways are expected to sensitize cancer cells to a variety of drugs. This hypothesis was tested by analyzing the diversity of drugs that synergize with FDA-approved inhibitors of the proteasome, RNA synthesis, and Akt-mTOR pathways. Indeed, the quantitative evaluation indicates that inhibitors of any of these signaling pathways can synergize with a more diverse set of pharmaceuticals, compared to compounds inhibiting targets distinct from the "weak points" pathways. Our findings described here imply that inhibitors of the "weak points" pathways should be considered as primary candidates in a search for synergistic drug combinations. [ABSTRACT FROM AUTHOR]

Ovarian cancer, a leading cause of gynecological cancer deaths globally, poses significant treatment challenges. Cisplatin (CDDP) is the first treatment choice for ovarian cancer and it is initially effective. However, 80% of ovarian cancer patients eventually relapse and develop resistance, resulting in chemotherapy failure. Therefore, finding new treatment combinations to overcome ovarian cancer resistance can provide a new tactic to improve the ovarian cancer patients' survival rate. We first identified activation of the Unfolded Protein Response (UPR) in CDDP-resistant ovarian cancer cells, implicating the IRE1α/XBP1 pathway in promoting resistance. Our findings demonstrate that inhibiting IRE1α signaling can re-sensitizes resistant cells to CDDP in vivo and in vitro, suggesting that IRE1α inhibitor used in conjunction with CDDP presumably could merge as a novel therapeutic strategy. Here, our research highlights the critical role of IRE1α signaling in mediating CDDP resistance, and paves the way for improved treatment options through combinatorial therapy. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects over 1% of population over age 60. It is defined by motor and nonmotor symptoms including a spectrum of cognitive impairments known as Parkinson's disease dementia (PDD). Currently, the only US Food and Drug Administration-approved treatment for PDD is rivastigmine, which inhibits acetylcholinesterase and butyrylcholinesterase increasing the level of acetylcholine in the brain. Due to its limited efficacy and side effect profile, rivastigmine is often not prescribed, leaving patients with no treatment options. PD has several derangements in neurotransmitter pathways (dopaminergic neurons in the nigrostriatal pathway, kynurenine pathway (KP), acetylcholine, α7 nicotinic receptor, and N-methyl-D-aspartate (NMDA) receptors) and rivastigmine is only partially effective as it only targets one pathway. Kynurenic acid (KYNA), a metabolite of tryptophan metabolism, affects the pathophysiology of PDD in multiple ways. Both galantamine (α7 nicotinic receptor) and memantine (antagonist of the NMDA subtype of the glutamate receptor) are KYNA modulators. When used in combination, they target multiple pathways. While randomized controlled trials (RCTs) with each drug alone for PD have failed, the combination of galantamine and memantine has demonstrated a synergistic effect on cognitive enhancement in animal models. It has therapeutic potential that has not been adequately assessed, warranting future randomized controlled trials. In this review, we summarize the KYNA-centric model for PD pathophysiology and discuss how this treatment combination is promising in improving cognitive function in patients with PDD through its action on KYNA. [ABSTRACT FROM AUTHOR]

Precision oncology has demonstrated the potential of drug combinations in effectively enhancing anti-tumor efficiency and controlling disease progression. Nonetheless, dose optimization in early-phase drug combination trials presents various challenges and is considerably more complex than single-agent dose optimization. To address this, we propose a surface-free design for exploring the optimal doses of combination therapy within the phase I–II framework. Rather than relying on parametric models to define the shape of toxicity and efficacy surfaces, our approach centers on characterizing dose-toxicity and dose-efficacy relationships between adjacent dose combinations using surface-free models. The proposed design encompasses a run-in phase, facilitating a swift exploration of the dose space, followed by a main phase where the dose-finding rule relies on the proposed surface-free model. Through extensive simulation studies, we have thoroughly examined the operating characteristics of this innovative design. The results demonstrate that our method exhibits desirable operating characteristics across a wide range of dose-toxicity and dose-efficacy relationships. [ABSTRACT FROM AUTHOR]

Introduction: Rhodococcus equi causes life-threatening respiratory disease in foals. The standard treatment typically involves a combination of rifampicin and a macrolide antibiotic. Although previous studies have demonstrated the in vitro activity of these antibiotics against Rhodococcus equi , the tested concentrations often do not reflect those achievable in foals. Material and Methods: Therefore, this study was performed to evaluate the in vitro bactericidal activity of rifampicin, doxycycline, and four macrolides (clarithromycin, azithromycin, gamithromycin and tulathromycin) individually and in combination, at concentrations observed at the target site of infection in foals. Additionally, we investigated the efficacy of these antibiotics at different pH levels to replicate the conditions in the pulmonary epithelial lining fluid and within macrophages, where R. equi can reside. We assessed the activity of antibiotics against a virulent strain of R. equi by determining the minimum inhibitory concentration (MIC) and performing checkerboard and time-kill curve assays with drugs both alone and in combination. Results: Time-kill curves with rifampicin or doxycycline demonstrated a reduction in R. equi counts by more than 3 log10 CFU/mL. Among the macrolides, tulathromycin was ineffective, while clarithromycin achieved bacterial elimination within 24 h under both extracellular and intracellular conditions. Gamithromycin and azithromycin exhibited bactericidal activity only in extracellular conditions, with no effect on the bacteria at pH 5.8. The checkerboard assay did not reveal any strong synergistic or antagonistic effects for rifampicin or doxycycline when combined with macrolides. In time-kill curves performed with maximal local concentrations achievable in foals, the combinations of rifampicin or doxycycline with macrolides did not increase the bacterial killing rate compared with the drugs alone, except for the combination of rifampicin with azithromycin, which showed slightly faster activity. However, the lower concentrations of doxycycline and clarithromycin that might be present 24 h after treatment in foals were effective in killing bacteria under intracellular conditions only when used in combination, and not when used alone. Conclusion: Our study suggests that clarithromycin can be used either alone or with doxycycline and that its use in combination with rifampicin should be reconsidered. Nevertheless, further studies are required to assess the clinical efficacy and potential side effects of doxycycline in foals. [ABSTRACT FROM AUTHOR]

Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes. [ABSTRACT FROM AUTHOR]

Cholangiocarcinoma (CCA) presents a formidable therapeutic challenge due to its extensive heterogeneity and plasticity, which inevitably lead to acquired resistance to current treatments. However, recent evidence suggests that acquired drug resistance is associated with a fitness cost resulting from the myriad of acquired alterations under the selective pressure of the primary treatment. Consequently, CCA patients with acquired resistance are more susceptible to alternative therapies that are ineffective as monotherapies. This phenomenon, termed "acquired vulnerability," has garnered significant interest in drug development, as the acquired alterations could potentially be exploited therapeutically. This review elucidates the modes of acquired vulnerability, methods for identifying and exploiting acquired vulnerabilities in cancer (particularly in CCA), and strategies to enhance the clinical efficacy of drug combinations by leveraging the principle of acquired vulnerability. Identifying acquired vulnerabilities may pave the way for novel drug combinations to effectively treat highly heterogeneous and adaptable malignancies such as CCA. [ABSTRACT FROM AUTHOR]

The identification of protective agents for the treatment of neurodegenerative diseases is the mainstay therapeutic goal to modify the disease course and arrest the irreversible disability progression. Pharmacological therapies synergistically targeting multiple pathogenic pathways, including oxidative stress, mitochondrial dysfunction, and inflammation, are prime candidates for neuroprotection. Combination or synergistic therapy with melatonin, whose decline correlates with altered sleep/wake cycle and impaired glymphatic "waste clearance" system in neurodegenerative diseases, has a great therapeutic potential to treat inflammatory neurodegenerative states. Despite the protective outcomes observed in preclinical studies, mild or poor outcomes were observed in clinical settings, suggesting that melatonin combinations promoting synergistic actions at appropriate doses might be more suitable to treat multifactorial neurodegenerative disorders. In this review, we first summarize the key melatonin actions and pathways contributing to cell protection and its therapeutic implication in Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS). We remark the major controversies in the field, mostly generated by the lack of a common consensus for the optimal dosing, molecular targets, and toxicity. Then, we review the literature investigating the efficacy of melatonin combinations with approved or investigational neuroprotective agents and of melatonin‐containing hybrid molecules, both in vitro and in animal models of AD, PD, and MS, as well as the efficacy of add‐on melatonin in clinical settings. We highlight the rationale for such melatonin combinations with a focus on the comparison with single‐agent treatment and on the assays in which an additive or a synergistic effect has been achieved. We conclude that a better characterization of the mechanisms underlying such melatonin synergistic actions under neuroinflammation at appropriate doses needs to be tackled to advance successful clinical translation of neuroprotective melatonin combination therapies or melatonin‐based hybrid molecules. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the efficacy and safety of dapagliflozin versus placebo as an add‐on in patients with type 2 diabetes who did not achieve adequate glycaemic control with evogliptin and metformin combination. Patients and Methods: In this multicentre, randomized, double‐blind, placebo‐controlled Phase 3 trial, patients with glycated haemoglobin (HbA1c) levels ≥7.0% (≥53 mmol/mol) and ≤10.5% (≤91 mmol/mol) who had received stable‐dose metformin (≥1000 mg) and evogliptin (5 mg) for at least 8 weeks were randomized to receive dapagliflozin 10 mg or placebo once daily for 24 weeks. Participants continued treatment with metformin and evogliptin. The primary endpoint was change in HbA1c level after 24 weeks of treatment from baseline level. Results: In total, 198 patients were randomized, and 195 patients were included in the efficacy analyses (dapagliflozin: 96, placebo: 99). At Week 24, dapagliflozin significantly reduced HbA1c levels. The least squares mean difference in HbA1c level change from baseline after 24 weeks of treatment was −0.70% (−7.7 mmol/mol) (p < 0.0001). The proportion of participants achieving HbA1c <7.0% (≥53 mmol/mol) was higher in the dapagliflozin group than in the placebo group. Compared to placebo, dapagliflozin significantly reduced fasting plasma glucose, mean daily glucose, 2‐h postprandial plasma glucose, fasting insulin, uric acid and gamma‐glutamyl transferase levels, homeostatic model assessment for insulin resistance index, body weight, hepatic steatosis index, and albuminuria. Adiponectin level significantly increased from baseline level after 24 weeks of dapagliflozin treatment. Adverse event rates were similar in the two groups. Conclusion: Dapagliflozin add‐on to evogliptin plus metformin improved glycaemic control and was well tolerated by the target patients. [ABSTRACT FROM AUTHOR]

Background/Aim: Non-small-cell lung cancer (NSCLC) is the most frequently diagnosed malignancy and the first cause of cancer-related death. Thus, finding alternative therapeutic options is crucial. Drug repurposing offers therapeutic options in a simplified and affordable manner, especially to cancer patients in developing countries. Several drugs including antihistamines and beta-adrenergic receptor blockers (beta-blockers) display antiproliferative properties on cancer cells. Interestingly, NSCLC patients who had used either antihistamines or beta-blockers showed improved response to chemotherapy or reduced mortality in comparison to non-users of any of these drugs. However, combination therapy is gaining substantial interest in many cancers including non-EGFR mutated NSCLC. Here, we investigated the antineoplastic effect of the combination of the antihistamine loratadine, the beta-blocker nebivolol, and the tyrosine-kinase inhibitor gefitinib on NSCLC cell lines. Materials and Methods: A-549 and NCI-H1975 cell lines were used. The effect of nebivolol, gefitinib, and loratadine on the metabolic activity was studied using the MTT assay. The inhibitory concentrations (IC20 and IC50) were calculated and used in the drug-combination experiments. Apoptosis was investigated using flow cytometry; and cell survival using the colony formation assay. Results: The combination nebivolol-loratadine-gefitinib produced a significant synergistic effect on inhibiting the metabolic activity and colony formation, as well as on promoting apoptosis in both cell lines. Noteworthy, the effect on the cell line carrying the EGFR mutation (NCI-H1975) was very similar to the cell line that does not exhibit such mutation (A-549 cells). Conclusion: The nebivolol-gefitinib-loratadine combination may be a promising alternative for lung cancer treatment. [ABSTRACT FROM AUTHOR]

Drug combination therapy, involving the use of two or more drugs, has been widely employed to treat complex diseases such as cancer. It enhances therapeutic efficacy, reduces drug resistance, and minimizes side effects. However, traditional methods to identify effective drug combinations are time-consuming, costly, and less efficient than computational methods. Therefore, developing computational approaches to predict drug combinations has become increasingly important.In this paper, we developed the Random Walk with Restart for Drug Combination (RWRDC) model to predict effective drug combinations for cancer therapy. The RWRDC model offers a quantitative mathematical method for predicting the potential effective drug combinations. Cross-validation results indicate that the RWRDC model outperforms other predictive models, particularly in breast, colorectal, and lung cancer predictions across various performance metrics. We have theoretically proven the convergence of its algorithm and provided an explanation for the algorithm's rationality. A targeted case study on breast cancer further highlights the capability of RWRDC to identify effective drug combinations. These findings highlight our model as a novel and effective tool for discovering potential effective drug combinations, offering new possibilities in therapy. Additionally, the graph-based framework of RWRDC holds potential for predicting drug combinations in other complex diseases, expanding its utility in the medical field.

Abstract Candida albicans is a common opportunistic pathogen, causing infections ranging from superficial to bloodstream infections. The limited antifungal options and rising drug resistance challenge clinical treatment. We screened 98 essential oils and identified 48 with antifungal activity against Candida albicans at 1% concentration, determining their minimum inhibitory concentrations (MIC). Of these, 14 maintained fungicidal activity at lower concentrations (0.25% and 0.125%). 5 essential oils (Cinnamon, Satureja montana, Palmarosa, Lemon eucalyptus, and Honey myrtle) showed the highest inhibitory effects on stationary-phase Candida albicans and inhibited hyphae elongation. Synergistic effects were observed when combining Palmarosa with amphotericin B (AmB) against growing-phase Candida albicans, while Cinnamon and Satureja montana with AmB showed superior efficacy against stationary-phase infections. We identified the active components of 5 essential oils using gas chromatography-mass spectrometry (GC-MS) and found the following main constituents: Cinnamon primarily contains benzyl benzoate and eugenol, Satureja montana is dominated by carvacrol and cymene, Palmarosa features geraniol and geranyl acetate, Lemon eucalyptus includes dl-Isopulegol and citronellal, and Honey myrtle is characterized by citral and neral. Our results may aid in developing more effective antifungal treatments.

Abstract Cholangiocarcinoma (CCA) presents a formidable therapeutic challenge due to its extensive heterogeneity and plasticity, which inevitably lead to acquired resistance to current treatments. However, recent evidence suggests that acquired drug resistance is associated with a fitness cost resulting from the myriad of acquired alterations under the selective pressure of the primary treatment. Consequently, CCA patients with acquired resistance are more susceptible to alternative therapies that are ineffective as monotherapies. This phenomenon, termed “acquired vulnerability,” has garnered significant interest in drug development, as the acquired alterations could potentially be exploited therapeutically. This review elucidates the modes of acquired vulnerability, methods for identifying and exploiting acquired vulnerabilities in cancer (particularly in CCA), and strategies to enhance the clinical efficacy of drug combinations by leveraging the principle of acquired vulnerability. Identifying acquired vulnerabilities may pave the way for novel drug combinations to effectively treat highly heterogeneous and adaptable malignancies such as CCA.

Scheme of putative synergism between a cationic α-helix lipopeptide with conventional antibiotics. Minocycline (MC) is a protein production inhibitor, so it must get inside the bacterial cell to display antimicrobial activity. In the example, the resistance mechanism of this Gram-negative bacteria is an efflux pump, which pumps the antibiotic outside the cell. The lipopeptide would permeabilize the membrane thus producing the income of more antibiotic molecules to the cytoplasm, where they could finally get the target. [Display omitted] Antimicrobial resistance (AMR) is fast becoming a major global challenge in both hospital and community settings as many current antibiotics and treatment processes are under the threat of being rendered less effective or ineffective. Synergistic combination of an antibiotic and an aiding agent with a different set of properties provides an important but largely unexploited option to 'repurpose' existing biomaterial's space while addressing issues of potency, spectrum, toxicity and resistance in early stages of antimicrobial drug discovery. This work explores how to combine tetracycline/minocycline (TC/MC) with a broad-spectrum antimicrobial lipopeptide that has been designed to improve the efficiency of membrane targeting and intramembrane accumulation, thereby enhancing antimicrobial efficacy. Experimental measurements of fractional inhibition concentration index (FICI) were undertaken from binary antibiotic-lipopeptide combinations. Most FICI values were found to be lower than 0.5 against both Gram-positive and Gram-negative bacterial strains studied including 3 AMR strains, revealing strong synergetic effects via favorable membrane-lytic interactions. The antimicrobial actions of this type of binary combinations are featured by the fast time-killing and high TC/MC uptake, benefited from effective membrane-lytic disruptions by the lipopeptide. This study thus provides an important mechanistic understanding of the combined antibiotic-lipopeptide approach to improve the therapeutic potential of conventional antibiotics by illustrating how amphiphilic lipopeptide-antibiotic combinations interact with biological membranes, providing a promising alternative to combat AMR through rational design of lipopeptide as an aiding agent. [ABSTRACT FROM AUTHOR]

Abstract Background Drug combination treatments have proven to be a realistic technique for treating challenging diseases such as cancer by enhancing efficacy and mitigating side effects. To achieve the therapeutic goals of these combinations, it is essential to employ multi-targeted drug combinations, which maximize effectiveness and synergistic effects. Results This paper proposes ‘MultiComb’, a multi-task deep learning (MTDL) model designed to simultaneously predict the synergy and sensitivity of drug combinations. The model utilizes a graph convolution network to represent the Simplified Molecular-Input Line-Entry (SMILES) of two drugs, generating their respective features. Also, three fully connected subnetworks extract features of the cancer cell line. These drug and cell line features are then concatenated and processed through an attention mechanism, which outputs two optimized feature representations for the target tasks. The cross-stitch model learns the relationship between these tasks. At last, each learned task feature is fed into fully connected subnetworks to predict the synergy and sensitivity scores. The proposed model is validated using the O’Neil benchmark dataset, which includes 38 unique drugs combined to form 17,901 drug combination pairs and tested across 37 unique cancer cells. The model’s performance is tested using some metrics like mean square error ( $$MSE$$ MSE ), mean absolute error ( $$MAE$$ MAE ), coefficient of determination ( $${R}^{2}$$ R 2 ), Spearman, and Pearson scores. The mean synergy scores of the proposed model are 232.37, 9.59, 0.57, 0.76, and 0.73 for the previous metrics, respectively. Also, the values for mean sensitivity scores are 15.59, 2.74, 0.90, 0.95, and 0.95, respectively. Conclusion This paper proposes an MTDL model to predict synergy and sensitivity scores for drug combinations targeting specific cancer cell lines. The MTDL model demonstrates superior performance compared to existing approaches, providing better results.

Background and aims: One essential plant-based strategy to deal with infections is antimicrobial synergism, which can make antimicrobials more efficient. The aim of this study was to investigate the interaction between these extracts and two widely used antibiotics, meropenem and gentamicin, on two multidrug-resistant (MDR) bacteria, K. pneumoniae and A. baumannii, in vitro. Methods: Different concentrations of Rosa damascena Mill., Malva sylvestris L., and Zataria multiflora Boiss. hydroalcoholic extracts (2-fold serial dilution from 131072 to 256 μg/mL) were administered against two MDR bacteria, and their combination with gentamicin and meropenem (serial dilution from 32 to 0.015 μg/mL) was investigated by the resazurin-based microdilution and the checkerboard method. The phytochemical properties of the extracts were also examined, and the total phenolic content (TPC), total flavonoid content (TFC), anthocyanin content, and antioxidant capacity of the extracts were determined. Results: Z. multiflora and R. damascena showed high antibacterial activity, and their minimal inhibitory concentrations on Acinetobacter baumannii were 1024 and 2048 μg/mL, respectively. Z. multiflora also had high TFC, TPC, and antioxidant activity and demonstrated additive interaction with meropenem and gentamicin with fractional inhibitory concentrations of 1 and 0.75, respectively. Conclusion: We suggest the potency of Z. multiflora-antibiotic combinations in treating MDR A. baumannii after future clinical studies.

Abstract Background Olaparib is a PARP inhibitor inducing synthetic lethality in tumors with deficient homologous recombination (HRD) caused by BRCA1/2 mutations. The FDA has approved monotherapy for first-line platinum-sensitive, recurrent high-grade epithelial ovarian cancer. Combination therapy alongside DNA-damaging therapeutics is a promising solution to overcome the limited efficacy in patients with HRD. The present study was designed to develop topotecan- and olaparib-loaded liposomes (TLL and OLL) and assess the effectiveness of their combination in patient-derived ovarian cancer samples. Methods We used HEOC, four clear-cell tumors (EOC 1–4), malignant ascites, and an OCI-E1p endometrioid primary ovarian cancer cell line and performed NGS analysis of BRCA1/2 mutation status. Antiproliferative activity was determined with the MTT assay. The Chou-Talalay algorithm was used to investigate the in vitro pharmacodynamic interactions of TLLs and OLLs. Results The OLL showed significantly higher efficacy in all ovarian cancer types with wild-type BRCA1/2 than a conventional formulation, suggesting potential for increased in vivo efficacy. The TLL revealed substantially higher toxicity to EOC 1, EOC 3, ascites and lower toxicity to HEOC than the standard formulation, suggesting better therapeutic efficacy and safety profile. The combination of studied compounds showed a higher reduction in cell viability than drugs used individually, demonstrating a synergistic antitumor effect at most of the selected concentrations. Conclusions The concentration-dependent response of different ovarian cancer cell types to combination therapy confirms the need for in vitro optimization to maximize drug cytotoxicity. The OLL and TLL combination is a promising formulation for further animal studies, especially for eliminating epithelial ovarian cancer with wild-type BRCA1/2.

Background: Drug combination treatments have proven to be a realistic technique for treating challenging diseases such as cancer by enhancing efficacy and mitigating side effects. To achieve the therapeutic goals of these combinations, it is essential to employ multi-targeted drug combinations, which maximize effectiveness and synergistic effects. Results: This paper proposes 'MultiComb', a multi-task deep learning (MTDL) model designed to simultaneously predict the synergy and sensitivity of drug combinations. The model utilizes a graph convolution network to represent the Simplified Molecular-Input Line-Entry (SMILES) of two drugs, generating their respective features. Also, three fully connected subnetworks extract features of the cancer cell line. These drug and cell line features are then concatenated and processed through an attention mechanism, which outputs two optimized feature representations for the target tasks. The cross-stitch model learns the relationship between these tasks. At last, each learned task feature is fed into fully connected subnetworks to predict the synergy and sensitivity scores. The proposed model is validated using the O'Neil benchmark dataset, which includes 38 unique drugs combined to form 17,901 drug combination pairs and tested across 37 unique cancer cells. The model's performance is tested using some metrics like mean square error ( MSE ), mean absolute error ( MAE ), coefficient of determination ( R 2 ), Spearman, and Pearson scores. The mean synergy scores of the proposed model are 232.37, 9.59, 0.57, 0.76, and 0.73 for the previous metrics, respectively. Also, the values for mean sensitivity scores are 15.59, 2.74, 0.90, 0.95, and 0.95, respectively. Conclusion: This paper proposes an MTDL model to predict synergy and sensitivity scores for drug combinations targeting specific cancer cell lines. The MTDL model demonstrates superior performance compared to existing approaches, providing better results. [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer is the most commonly diagnosed cancer in women, and triple-negative breast cancer (TNBC) accounts for approximately 15%-20% of all breast cancers. TNBC is highly invasive and malignant. Due to the lack of relevant receptor markers, the prognosis of TNBC is poor and the five-year survival rate is low. Paclitaxel is the first-line drug for the treatment of TNBC, which can inhibit cell mitosis. However, many patients develop drug resistance during treatment, leading to chemotherapy failure. Therefore, finding new therapeutic combinations to overcome TNBC drug resistance can provide new strategies for improving the survival rate of TNBC patients. Methods: Cell viability assay, RT-qPCR, Colony formation assay, Western blot, and Xenogeneic transplantation methods were used to investigate roles and mechanisms of IRE1α/XBP1s pathway in the paclitaxel-resistant TNBC cells, and combined paclitaxel and IRE1α inhibitor in the treatment of TNBC was examined in vitro and in vivo. Results: We found activation of UPR in paclitaxel-resistant cells, confirming that IRE1α/XBP1 promotes paclitaxel resistance in TNBC. In addition, we demonstrated that the combination of paclitaxel and IRE1α inhibitors can synergistically inhibit the proliferation of TNBC tumors both in vitro and in vivo,suggesting that IRE1α inhibitors combined with paclitaxel may be a new treatment option for TNBC. Conclusions: In this study, we demonstrated the important role of IRE1α signaling in mediating paclitaxel resistance and identified that combination therapies targeting IRE1α signaling could overcome paclitaxel resistance and enhance chemotherapy efficacy. [ABSTRACT FROM AUTHOR]

The metabolic enzyme methionine adenosyltransferase 2A (MAT2A) was found to elicit synthetic lethality in methylthioadenosine phosphorylase (MTAP)‐deleted cancers, which occur in about 15% of all cancers. Here, we described a novel MAT2A inhibitor, SCR‐7952 with potent and selective antitumor effects on MTAP‐deleted cancers in both in vitro and in vivo. The cryo‐EM data indicated the high binding affinity and the allosteric binding site of SCR‐7952 on MAT2A. Different from AG‐270, SCR‐7952 exhibited little influence on metabolic enzymes and did not increase the plasma levels of bilirubin. A systematic evaluation of combination between SCR‐7952 and different types of protein arginine methyltransferase 5 (PRMT5) inhibitors indicated remarkable synergistic interactions between SCR‐7952 and the S‐adenosylmethionine‐competitive or the methylthioadenosine‐cooperative PRMT5 inhibitors, but not substrate‐competitive ones. The mechanism was via the aggravated inhibition of PRMT5 and FANCA splicing perturbations. These results indicated that SCR‐7952 could be a potential therapeutic candidate for the treatment of MTAP‐deleted cancers, both monotherapy and in combination with PRMT5 inhibitors. [ABSTRACT FROM AUTHOR]

Nanomedicine holds promise for potentiating drug combination therapies. Increasing (pre)clinical evidence is available exemplifying the value of co-formulating and co-delivering different drugs in modular nanocarriers. Taxanes like paclitaxel (PTX) are widely used anticancer agents, and commonly combined with corticosteroids like dexamethasone (DEX), which besides for suppressing inflammation and infusion reactions, are increasingly explored for modulating the tumor microenvironment towards enhanced nano-chemotherapy delivery and efficacy. We here set out to develop a size- and release rate-tunable polymeric micelle platform for co-delivery of taxanes and corticosteroids. We synthesized amphiphilic mPEG-b-p(HPMAm-Bz) block copolymers of various molecular weights and used them to prepare PTX and DEX single- and double-loaded micelles of different sizes. Both drugs could be efficiently co-encapsulated, and systematic comparison between single- and co-loaded formulations demonstrated comparable physicochemical properties, encapsulation efficiencies, and release profiles. Larger micelles showed slower drug release, and DEX release was always faster than PTX. The versatility of the platform was exemplified by co-encapsulating two additional taxane-corticosteroid combinations, demonstrating that drug hydrophobicity and molecular weight are key properties that strongly contribute to drug retention in micelles. Altogether, our work shows that mPEG-b-p(HPMAm-Bz) polymeric micelles serve as a tunable and versatile nanoparticle platform for controlled co-delivery of taxanes and corticosteroids, thereby paving the way for using these micelles as a modular carrier for multidrug nanomedicine. [ABSTRACT FROM AUTHOR]

Aim: We retrospectively evaluated the effect of dabrafenib/trametinib combination in patients with BRAF-mutated non-small-cell lung cancer (NSCLC) treated in a single center from 2017 to 2022. Patients: The response and safety data of 42 patients (27 treated in first-line and 15 as second/subsequent lines) were analyzed. Results: The objective response was 73.8%, with no differences between patients undergoing first- or second-line. A longer, statistically significant median progression-free survival (PFS) was observed in patients receiving the combination in first-line vs those in the second/subsequent lines (19.9 months [95% CI: 19.7–20] vs 13.1 months [95% CI: 8.6–17.6], respectively; p = 0.012). The median overall survival (OS) was 29.9 months (95% CI: 14.1–45.7) for patients treated with the combination in first-line and 22.4 months (95% CI: 14.6–30.2) for those treated in subsequent lines. The combination was well tolerated Conclusion: We confirm the efficacy of dabrafenib/trametinib in BRAF-V600-mutated NSCLC. First-line treatment of dabrafenib and trametinib is effective in non-small-cell lung cancer patients with V600 BRAF mutation. A longer, statistically significant median progression-free survival was found in patients receiving the combination in first-line versus those in the second/subsequent lines. The efficacy is confirmed in real-world setting from a single center. More than 70% of the patients reached a partial response. The combination is safe with dose reduction necessary only in two patients for pyrexia. The treatment is feasible in elder patients. [ABSTRACT FROM AUTHOR]

Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne virus that causes the severe fever thrombocytopenia syndrome, which manifests as fever and haemorrhage, accompanied by severe neurological complications. To date, no specific antiviral drugs have been approved for this indication. Herein, we investigated whether vitamin D derivatives inhibit SFTSV both in vitro and in vivo. An in vitro study demonstrated that vitamin D derivatives significantly suppressed viral RNA replication, plaque formation, and protein expression in a dosedependent manner. Subsequently, in vivo studies revealed that doxercalciferol and alfacalcidol were associated with increased survival and reduced viral RNA load in the blood. Time-of-addition assay suggested that vitamin D derivatives primarily acted during the post-entry phase of SFTSV infection. However, cytopathic effect protective activity was not observed in RIG-I immunodeficient cell line Huh7.5, and the administration of vitamin D derivatives did not improve the survival rates or reduce the blood viral loads in adult A129 mice. Further transcriptome exploration into the antiviral mechanism revealed that alfacalcidol stimulates host innate immunity to exert antiviral effects. To expand the application of vitamin D derivatives, in vitro and in vivo drug combination assays were performed, which highlighted the synergistic effects of vitamin D derivatives and T-705 on SFTSV. The combination of alfacalcidol and T-705 significantly enhanced the therapeutic effects in mice. This study highlights the potential of vitamin D derivatives against SFTSV and suggests that they may have synergistic effects with other compounds used in the treatment of SFTSV infection. [ABSTRACT FROM AUTHOR]

The overactivation of Janus kinases 2 (JAK2) by gain-of-function mutations in the JAK2, Myeloproliferative leukemia virus oncogene, or Calreticulin genes are the most important factor in the development of Philadelphia-negative myeloproliferative neoplasms (MPNs). The discovery of the JAK2V617F mutation is a significant breakthrough in understanding the pathogenesis of MPNs, and inhibition of JAK2 abnormal activation has become one of the most effective strategies against MPNs. Currently, three JAK2 inhibitors for treating MPNs have been approved, and several are being evaluated in clinical trials. However, persistent challenges in terms of drug resistance and off-target effects remain unresolved. In this review, we introduce and classify the available JAK2 inhibitors in terms of their mechanisms and clinical considerations. Additionally, through an analysis of target points, binding modes, and structure–activity inhibitor relationships, we propose strategies such as combination therapy and allosteric inhibitors to overcome specific challenges. This review offers valuable insights into current trends and future directions for optimal management of MPNs using JAK2 inhibitors. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-view ensemble predictor model that is designed to address the challenge of determining synergistic drug combinations by predicting both the synergy score value values and synergy class label of drug combinations with cancer cell lines. The proposed methodology involves representing drug features through four distinct views: Simplified Molecular-Input Line-Entry System (SMILES) features, molecular graph features, fingerprint features, and drug-target features. On the other hand, cell line features are captured through four views: gene expression features, copy number features, mutation features, and proteomics features. To prevent overfitting of the model, two techniques are employed. First, each view feature of a drug is paired with each corresponding cell line view and input into a multi-task attention deep learning model. This multi-task model is trained to simultaneously predict both the synergy score value and synergy class label. This process results in sixteen input view features being fed into the multi-task model, producing sixteen prediction values. Subsequently, these prediction values are utilized as inputs for an ensemble model, which outputs the final prediction value. The 'MVME' model is assessed using the O'Neil dataset, which includes 38 distinct drugs combined across 39 distinct cancer cell lines to output 22,737 drug combination pairs. For the synergy score value, the proposed model scores a mean square error (MSE) of 206.57, a root mean square error (RMSE) of 14.30, and a Pearson score of 0.76. For the synergy class label, the model scores 0.90 for accuracy, 0.96 for precision, 0.57 for kappa, 0.96 for the area under the ROC curve (ROC-AUC), and 0.88 for the area under the precision-recall curve (PR-AUC). Scientific contribution: This paper presents an enhanced synergistic drug combination model by utilizing four different feature views for drugs and four views for cancer cell lines. Each view is then input into a multi-task deep learning model to predict both the synergy score and class label simultaneously. To address the challenge of managing diverse views and their corresponding prediction values while avoiding overfitting, an ensemble model is applied. [ABSTRACT FROM AUTHOR]

Background and aims: One essential plant-based strategy to deal with infections is antimicrobial synergism, which can make antimicrobials more efficient. The aim of this study was to investigate the interaction between these extracts and two widely used antibiotics, meropenem and gentamicin, on two multidrug-resistant (MDR) bacteria, K. pneumoniae and A. baumannii, in vitro. Methods: Different concentrations of Rosa damascena Mill., Malva sylvestris L., and Zataria multiflora Boiss. hydroalcoholic extracts (2-fold serial dilution from 131072 to 256 μg/mL) were administered against two MDR bacteria, and their combination with gentamicin and meropenem (serial dilution from 32 to 0.015 μg/mL) was investigated by the resazurin-based microdilution and the checkerboard method. The phytochemical properties of the extracts were also examined, and the total phenolic content (TPC), total flavonoid content (TFC), anthocyanin content, and antioxidant capacity of the extracts were determined. Results: Z. multiflora and R. damascena showed high antibacterial activity, and their minimal inhibitory concentrations on Acinetobacter baumannii were 1024 and 2048 μg/mL, respectively. Z. multiflora also had high TFC, TPC, and antioxidant activity and demonstrated additive interaction with meropenem and gentamicin with fractional inhibitory concentrations of 1 and 0.75, respectively. Conclusion: We suggest the potency of Z. multiflora-antibiotic combinations in treating MDR A. baumannii after future clinical studies. [ABSTRACT FROM AUTHOR]

Clotrimazole (CLZ), traditionally an antifungal agent, unveils promising avenues in cancer therapy, particularly in addressing bladder and prostate cancers. In vitro assessments underscore its remarkable efficacy as a standalone treatment, significantly diminishing cancer cell viability. Mechanistically, CLZ operates through multifaceted pathways, including the inhibition of Ca2+-dependent K+ channels, suppression of glycolysis-related enzymes, and modulation of the ERK-p65 signaling cascade, thus underscoring its potential as a versatile therapeutic agent. Our investigation sheds light on intriguing observations regarding the resilience of UM-UC-5 bladder cancer cells against high doses of paclitaxel (PTX), potentially attributed to heightened levels of the apoptosis-regulating protein Mcl-1. However, synergistic studies demonstrate that the combination of Doxorubicin (DOXO) and CLZ emerges as particularly potent, especially in prostate cancer contexts. This effectiveness could be associated with the inhibition of drug efflux mediated by multidrug resistance-associated protein 1 (MRP1), underscoring the importance of exploring combination therapies in cancer treatment paradigms. In essence, our findings shed light on the anticancer potential of CLZ, emphasizing the significance of tailored approaches considering specific cancer types and molecular pathways in drug repurposing endeavors. While further validation and clinical exploration are warranted, the insights gleaned from this study offer promising prospects for enhancing cancer therapy efficacy. [ABSTRACT FROM AUTHOR]

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework's reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment. [ABSTRACT FROM AUTHOR]

Influenza viruses remain a major threat to human health. Four classes of drugs have been approved for the prevention and treatment of influenza infections. Oseltamivir, a neuraminidase inhibitor, is a first-line anti-influenza drug, and baloxavir is part of the newest generation of anti-influenza drugs that targets the viral polymerase. The emergence of drug resistance has reduced the efficacy of established antiviral drugs. Combination therapy is one of the options for controlling drug resistance and enhancing therapeutical efficacies. Here, we evaluate the antiviral effects of baloxavir combined with neuraminidase inhibitors (NAIs) against wild-type influenza viruses, as well as influenza viruses with drug-resistance mutations. The combination of baloxavir with NAIs led to significant synergistic effects; however, the combination of baloxavir with laninamivir failed to result in a synergistic effect on influenza B viruses. Considering the rapid emergence of drug resistance to baloxavir, we believe that these results will be beneficial for combined drug use against influenza. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is one of the leading causes of high mortality rates in women worldwide. Although advancements have been made in the design of therapeutic strategies and drug discovery, drug resistance remains one of the key challenges. One of the ways to overcome drug resistance is finding potential drug combinations since the efficacy of combined drugs is higher than their individual efficacies if the combination is a synergistic pair. Therefore, the current study uses a BC patient‐derived xenograft (PDX) dataset to evaluate the effects of various cancer drugs on breast cancer in vivo models. The drug effects are further validated by four machine learning models, namely Elastic Net, Least Absolute Shrinkage and Selection (LASSO), Support Vector Machine (SVM), Random Forests (RF), as well as exploring the shortlisted drugs in combination with paclitaxel, a baseline drug for enhanced efficacy on tumor volume reduction. Additionally, the study also shortlists the top 50 in vivo biomarkers correlated with the effects of the drugs. The outcomes could be significantly important for the design of an effective anti‐breast cancer therapy. [ABSTRACT FROM AUTHOR]

Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. It is important to discover new therapeutic regimens for treating CRC. Depression is known to be an important complication of cancer diseases. Repurposing antidepressants into anticancer drugs and exploring the combinational efficacy of antidepressants and chemotherapy are potentially good options for developing CRC treatment regimens. In this study, sertraline, an antidepressant drug, and paclitaxel, an anticancer drug, were chosen to study their antitumor effects in the treatment of colorectal cancer, alone or in combination, and to explore their underlying mechanisms. The data showed that sertraline exerted a dose-dependent cytotoxic effect on MC38 and CT26 colorectal cancer cell lines with IC50 values of 10.53 μM and 7.47 μM, respectively. Furthermore, sertraline synergistically sensitized chemotherapeutic agent paclitaxel efficacy in CRC cells with combination index (CI) values at various concentrations consistently lower than 1. Sertraline remarkably augmented paclitaxel-induced autophagy by increasing autophagosome formation indicated by elevated LC3-II/I ratio and promoting autophagic flux by degrading autophagy cargo receptor SQSTM1/p62, which may explain the synergistically cytotoxic effect of sertraline and paclitaxel combination therapy on CRC cells. This study provides important evidence to support repurposing sertraline as an anticancer agent and suggests a novel combinational regimen for effectively treating CRC as well as in the simultaneous treatment of CRC and depression. [ABSTRACT FROM AUTHOR]

Simple Summary: Combining drugs is crucial for enhancing anti-cancer responses. However, the potential of pre-clinical data in identifying suitable combinations and dosage is often underutilized. In this study, we leverage pre-clinical in vitro cell line drug response data and computational modeling of signal transduction and of pharmacokinetics to elucidate distinct dose requirements for the combination of pan-RAF and MEK inhibitors in melanoma. Our findings reveal a more synergistic but narrower dosing landscape in NRAS vs. BRAF mutant melanoma, which we link to a mechanism of adaptive resistance through negative feedback. Further, our analysis suggests the importance of drug dosing strategies to optimize synergy based on mutational context yet highlights the real-world challenges of maintaining a narrow dose range. This approach establishes a framework for translational investigation of drug responses in the refinement of combination therapy, balancing the potential for synergy and practical feasibility in cancer treatment planning. Purpose: This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. Results: In a drug combination screen of 43 melanoma cell lines, we identified specific dosage landscapes of panRAF and MEK inhibitors for NRAS vs. BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma (mean Bliss score of 0.27 in NRAS vs. 0.1 in BRAF mutants). Computational modeling and follow-up molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated the in vivo translatability of in vitro dose–response maps by predicting tumor growth in xenografts with high accuracy in capturing cytostatic and cytotoxic responses. We analyzed the pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Conclusion: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Overall, this work presents a framework to aid dose selection in drug combinations. [ABSTRACT FROM AUTHOR]

Antibiotic resistance is a growing global problem that requires innovative therapeutic approaches and strategies for administering antibiotics. One promising approach is combination therapy, in which two or more drugs are combined to combat an infection. Along this line, the combination of antimicrobial peptides (AMPs) with conventional antibiotics has gained attention mainly due to the complementary mechanisms of action of AMPs and conventional antibiotics. In this article, we review both in vitro and in vivo studies that explore the synergy between AMPs and antibiotics. We highlight several mechanisms through which synergy is observed in in vitro experiments, including increasing membrane permeability, disrupting biofilms, directly potentiating antibiotic efficacy, and inhibiting resistance development. Moreover, in vivo studies reveal additional mechanisms such as enhanced/modulated immune responses, reduced inflammation, and improved tissue regeneration. Together, the current literature demonstrates that AMP-antibiotic combinations can substantially enhance efficacy of antibiotic therapies, including therapies against resistant bacteria, which represents a valuable enhancement to current antimicrobial strategies. [ABSTRACT FROM AUTHOR]

Background: Olaparib is a PARP inhibitor inducing synthetic lethality in tumors with deficient homologous recombination (HRD) caused by BRCA1/2 mutations. The FDA has approved monotherapy for first-line platinum-sensitive, recurrent high-grade epithelial ovarian cancer. Combination therapy alongside DNA-damaging therapeutics is a promising solution to overcome the limited efficacy in patients with HRD. The present study was designed to develop topotecan- and olaparib-loaded liposomes (TLL and OLL) and assess the effectiveness of their combination in patient-derived ovarian cancer samples. Methods: We used HEOC, four clear-cell tumors (EOC 1–4), malignant ascites, and an OCI-E1p endometrioid primary ovarian cancer cell line and performed NGS analysis of BRCA1/2 mutation status. Antiproliferative activity was determined with the MTT assay. The Chou-Talalay algorithm was used to investigate the in vitro pharmacodynamic interactions of TLLs and OLLs. Results: The OLL showed significantly higher efficacy in all ovarian cancer types with wild-type BRCA1/2 than a conventional formulation, suggesting potential for increased in vivo efficacy. The TLL revealed substantially higher toxicity to EOC 1, EOC 3, ascites and lower toxicity to HEOC than the standard formulation, suggesting better therapeutic efficacy and safety profile. The combination of studied compounds showed a higher reduction in cell viability than drugs used individually, demonstrating a synergistic antitumor effect at most of the selected concentrations. Conclusions: The concentration-dependent response of different ovarian cancer cell types to combination therapy confirms the need for in vitro optimization to maximize drug cytotoxicity. The OLL and TLL combination is a promising formulation for further animal studies, especially for eliminating epithelial ovarian cancer with wild-type BRCA1/2. [ABSTRACT FROM AUTHOR]

Background: Through an analysis of the Food and Drug Administration Adverse Event Reporting System (FAERS), we explored the signal strength of adverse reactions (ADRs) related to myopathy caused by the combination of colchicine and statins and gained insight into the characteristics of these myopathy related ADRs. Methods: We extracted data from the FAERS database about ADRs in individuals with myopathy resulting from the combination of colchicine and statins. The analysis was conducted for the period spanning from January 2004 to December 2023 using the reported odds ratio (ROR) and information component (IC) methods to assess muscle-related ADR signals. Results: A total of 18,386 reports of statin myopathy-associated adverse reactions, 348 colchicine myopathy-associated adverse reactions, and 461 muscle-associated adverse reactions due to the combination of the two were collected; the strongest signals of statin myotoxicity events were for necrotizing myositis (ROR 50.47, 95% CL 41.74-61.01; IC 3.70 95% CL 3.25-4.08); the strongest signal for colchicine myotoxicity events was toxic myopathy (ROR 32.50, 95% CL 19.74-53.51; IC 4.97 95% CL 1.89-5.10), and the strongest signal for statins combined with colchicine was toxic myopathy (ROR 159.85, 95% CL 111.60-228.98; IC 7.22 95% CL 3.59-5.9); muscle-related adverse reactions signals were meaningful when the two drugs were combined in the order of colchicine combined with fluvastatin (ROR 187.38, 95% CL 96.68-363.17; IC 6.99 95% CL 1.65-5.68); colchicine combined with simvastatin in 135 cases (ROR 30.08. 95% CL 25.25-35.85; IC 4.80 95% CL 3.96-5.12); and colchicine combined with rosuvastatin (ROR 25.73, 95% CL 20.16-32.83; IC 4.59 95% CL 3.38-4.98) versus colchicine combined with atorvastatin (ROR 25.73, 95% CL 22.33-29.66; IC 4.59 95% CL 3.97-4.91) with almost identical signal intensity, followed by colchicine combined with pravastatin (ROR 13.67, 95% CL 9.17-20.37; IC 3.73 95% CL 1.87-4.47), whereas no signals were generated for lovastatin or pitavastatin. Conclusion: Similar ADRs can occur when colchicine and statins are used individually or in combination; however, the strength of these reactions may differ. To minimize the risk of drug interactions, statins with less potential interactions, such as lovastatin, pitavastatin, and pravastatin, should be chosen, and myopathy-related indices and symptoms should be closely monitored during use. [ABSTRACT FROM AUTHOR]

In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs—those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non‐interacting drugs and implement the two most common models for drug independence—Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a 'fair' comparison. Yet, exceptions exist for drugs with steep dose–response curves. Combining a bacteriostatic and a bactericidal drug which can kill non‐replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio—even if not always optimal—is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single‐cell level. [ABSTRACT FROM AUTHOR]

This study investigated the impact of Valencia KK4-type peanut skin ethanolic extract (KK4-PSE) combined with cisplatin or 5-fluorouracil (5-FU) on HeLa cells in vitro and in xenograft models. At exposure times of 24, 48 and 72 h, KK4-PSE inhibited the growth of HeLa cells with a half maximal inhibitory concentration (IC50) of 79.43 ± 0.54, 55.55 ± 1.57 and 41.32 ± 0.74 µg/mL, respectively. Drug interactions evaluated by the Chou–Talalay method demonstrated that KK4-PSE enhanced antiproliferative activity of 5-FU against HeLa cells with combination index (CI) values of 0.49 (48 h) and 0.60 (72 h), indicating a synergistic effect, while KK4-PSE combined with cisplatin exhibited an additive effect (CI = 1.02) at 72 h, and an antagonistic effect at 24 and 48 h exposures (CI = 1.12 and 1.18, respectively). In nude mouse xenograft models, the combination of 5-FU and KK4-PSE markedly reduced HeLa tumor weights compared with the control and single agent treatments groups. The combination of KK4-PSE and 5-FU achieved greater tumor growth inhibition than that of the KK4-PSE–cisplatin combination. KK4-PSE mitigated hepatotoxicity induced by both cisplatin and 5-FU in nude mice. The spleen hyaloserositis was significantly reduced in the combination treatment of 5-FU and KK4-PSE. These results suggest that KK4-PSE has the potential to limit cervical cancer cell proliferation while reducing the toxicity of cisplatin and 5-FU. [ABSTRACT FROM AUTHOR]

Echinococcosis is a zoonotic parasitic disease caused by the larvae of echinococcus tapeworms infesting the human body. Drug combination therapy is highly valued for the treatment of echinococcosis because of its potential to overcome resistance and enhance the response to existing drugs. Traditional methods of identifying drug combinations via biological experimentation is costly and time-consuming. Besides, the scarcity of existing drug combinations for echinococcosis hinders the development of computational methods. In this study, we propose a transfer learning-based model, namely TransferBAN-Syn, to identify synergistic drug combinations against echinococcosis based on abundant information of drug combinations against parasitic diseases. To the best of our knowledge, this is the first work that leverages transfer learning to improve prediction accuracy with limited drug combination data in echinococcosis treatment. Specifically, TransferBAN-Syn contains a drug interaction feature representation module, a disease feature representation module, and a prediction module, where the bilinear attention network is employed in the drug interaction feature representation module to deeply extract the fusion feature of drug combinations. Besides, we construct a special dataset with multi-source information and drug combinations for parasitic diseases, including 21 parasitic diseases and echinococcosis. TransferBAN-Syn is designed and initially trained on the abundant data from the 21 parasitic diseases, which serves as the source domain. The parameters in the feature representation modules of drug interactions and diseases are preserved from this source domain, and those in the prediction module are then fine-tuned to specifically identify the synergistic drug combinations for echinococcosis in the target domain. Comparison experiments have shown that TransferBAN-Syn not only improves the accuracy of predicting echinococcosis drug combinations but also enhances generalizability. Furthermore, TransferBAN-Syn identifies potential drug combinations that hold promise in the treatment of echinococcosis. TransferBAN-Syn not only offers new synergistic drug combinations for echinococcosis but also provides a novel approach for predicting potential drug pairs for diseases with limited combination data.

IntroductionRhodococcus equi causes life-threatening respiratory disease in foals. The standard treatment typically involves a combination of rifampicin and a macrolide antibiotic. Although previous studies have demonstrated the in vitro activity of these antibiotics against Rhodococcus equi, the tested concentrations often do not reflect those achievable in foals.Material and MethodsTherefore, this study was performed to evaluate the in vitro bactericidal activity of rifampicin, doxycycline, and four macrolides (clarithromycin, azithromycin, gamithromycin and tulathromycin) individually and in combination, at concentrations observed at the target site of infection in foals. Additionally, we investigated the efficacy of these antibiotics at different pH levels to replicate the conditions in the pulmonary epithelial lining fluid and within macrophages, where R. equi can reside. We assessed the activity of antibiotics against a virulent strain of R. equi by determining the minimum inhibitory concentration (MIC) and performing checkerboard and time-kill curve assays with drugs both alone and in combination.ResultsTime-kill curves with rifampicin or doxycycline demonstrated a reduction in R. equi counts by more than 3 log10 CFU/mL. Among the macrolides, tulathromycin was ineffective, while clarithromycin achieved bacterial elimination within 24 h under both extracellular and intracellular conditions. Gamithromycin and azithromycin exhibited bactericidal activity only in extracellular conditions, with no effect on the bacteria at pH 5.8. The checkerboard assay did not reveal any strong synergistic or antagonistic effects for rifampicin or doxycycline when combined with macrolides. In time-kill curves performed with maximal local concentrations achievable in foals, the combinations of rifampicin or doxycycline with macrolides did not increase the bacterial killing rate compared with the drugs alone, except for the combination of rifampicin with azithromycin, which showed slightly faster activity. However, the lower concentrations of doxycycline and clarithromycin that might be present 24 h after treatment in foals were effective in killing bacteria under intracellular conditions only when used in combination, and not when used alone.ConclusionOur study suggests that clarithromycin can be used either alone or with doxycycline and that its use in combination with rifampicin should be reconsidered. Nevertheless, further studies are required to assess the clinical efficacy and potential side effects of doxycycline in foals.