Synergistic drug combinations tend to improve therapeutically relevant selectivity

The unfolded features on the synchronized fashion of gut microbiota and Drynaria rhizome against obesity via integrated pharmacology

Drynaria rhizome (DR) is used as a natural remedy to ameliorate obesity (OB) in East Asia; in parallel, the gut microbiota (GM) might exert a positive impact on OB through their metabolites. This study elucidates the orchestrated effects of DR and GM on OB. DR-GM, - a key signaling pathway-target-metabolite (DGSTM) networks were used to unveil the relationship between DR and GM, and Molecular Docking Test (MDT) and Density Functional Theory (DFT) were adopted to underpin the uppermost molecules. The NR1H3 (target) - 3-Epicycloeucalenol (ligand), and PPARG (target) - Clionasterol (ligand) conjugates from DR, FABP3 (target) - Ursodeoxycholic acid, FABP4 (target) - Lithocholic acid (ligand) or Deoxycholic acid (ligand), PPARA (target) - Equol (ligand), and PPARD (target) - 2,3-Bis(3,4-dihydroxybenzyl)butyrolactone (ligand) conjugates from GM formed the most stable conformers via MDT and DFT. Overall, these findings suggest that DR-GM might be a promising ameliorator on PPAR signaling pathway against OB.

Oral delivery of solid lipid nanoparticles surface decorated with hyaluronic acid and bovine serum albumin: A novel approach to treat colon cancer through active targeting

The present study aims to prepare and evaluate solid lipid nanoparticles (SLNs) loaded with irinotecan (IRN) drug and daidzein (DZN) isoflavonoid and surface coated with ligand materials such as hyaluronic acid (HA) and bovine serum albumin (BSA) with additional coating of chitosan for active targeting to receptors present on colon surface epithelium for oral targeted delivery. The optimized batch was evaluated for particle size, zeta potential exhibiting nanometric size with good entrapment efficiency. Nanoparticles were found to be spherical. FTIR and DSC revealed that all the excipients and formulation were compatabile to each other and showed better encapsulation exhibiting amorphous and crystallinity forms. In vitro drug release of SLNs confirmed that initially a burst release, followed by sustained release pattern was exhibited. Cell lines studied performed on HT-29 cells showed demonstrated that conjugated SLNs inhibited cytotoxicity at 75 μg/ml, indicating that cells were taken up through a receptor-mediated endocytosis process. Cell cycle analysis showed that cell arrest was done at 67.8 % (G0/G1 phase) and inhibited apoptosis by 56 %. Further during In vivo studies, RT-PCR study revealed downregulation of Carcinoembryonic antigen (CEA), a non-specific serum biomarker overexpressed in tumor cells and upregulation of pro-inflammatory cytokine TNF-α. Histopathological study revealed that conjugated (HA-BSA) coated with chitosan SLNs restored normal mucosa and colon architecture, depicting all mucosal layers. Hence, these conjugated SLNs may serve as a novel combination for the treatment of colon cancer.

Drug-Drug Interactions and Synergy: From Pharmacological Models to Clinical Application

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. SIGNIFICANCE STATEMENT: Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug-drug interactions research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Intrinsic Synergy and Selectivity for the Inhibition of Cancer Cell Growth Generated by a Polymer Ligand of Proximal Enzymes

A fundamental understanding of the design of polymer ligands of proximal enzymes is essential for the precise targeting of cancer cells, but it is still in its infancy. In this study, we systematically investigated the contribution of the chain length, ligand density, and ligand ratio of proximal enzyme-targeted polymers to the efficacy, synergy, and selectivity for the inhibition of cancer cell proliferation. The results revealed that employing a moderate chain length as a scaffold allowed for an intrinsically high efficacy and synergy of proximal enzyme-targeted polymers, in contrast to single enzyme-targeted polymers that prefer longer chain length for efficacy. The synergy obtained in proximal enzyme targeting was not provided by the combination of the corresponding small molecules. Moreover, the maturation of the synergistic efficacy of the proximal enzyme-targeted polymers also improved selectivity. This study proposes a rational design for polymer inhibitors and/or ligands for cancer cells with a high efficacy and selectivity.

Ionizable Drugs Enable Intracellular Delivery of Co-Formulated siRNA

Targeting complementary pathways in diseases such as cancer can be achieved with co-delivery of small interfering ribonucleic acid (siRNA) and small molecule drugs; however, current formulation strategies are typically limited to one, but not both. Here, ionizable small molecule drugs and siRNA are co-formulated in drug-rich nanoparticles. Ionizable analogs of the selective estrogen receptor degrader fulvestrant self-assemble into colloidal drug aggregates and cause endosomal disruption, allowing co-delivery of siRNA against a non-druggable target. siRNA is encapsulated in lipid-stabilized, drug-rich colloidal nanoparticles where the ionizable lipid used in conventional lipid nanoparticles is replaced with an ionizable fulvestrant analog. The selection of an appropriate phospholipid and formulation buffer enables endocytosis and potent reporter gene knockdown in cancer cells. Importantly, siRNA targeting cyclin E1 is effectively delivered to drug-resistant breast cancer cells, demonstrating the utility of this approach. This strategy opens the possibility of using ionizable drugs to co-deliver RNA and ultimately improve therapeutic outcomes.

Inhibition of the ATP synthase increases sensitivity of Escherichia coli carrying mcr-1 to polymyxin B

Bacterial infections caused by multidrug-resistant (MDR) gram-negative strains carrying the mobile colistin resistance gene mcr-1 are serious threats to world public health due to the lack of effective treatments. Inhibition of the ATP synthase makes bacteria such as Staphylococcus aureus and Klebsiella pneumoniae more sensitive to polymyxin. This provides new strategies for treating infections caused by polymyxins-resistant bacteria carrying mcr-1. Six mcr-1-positive strains were isolated from clinical samples, and all were identified as Escherichia coli. Here we investigated several ATP synthase inhibitors, N,N'-dicyclohexylcarbodiimide (DCCD), resveratrol, and piceatannol, for their antibacterial effects against the mcr-1-positive strains combined with polymyxin B (POL). Checkerboard assay, time-kill assay, biofilm inhibition and eradication assay indicated the significant synergistic effect of ATP synthase inhibitors/POL combination in vitro. Meanwhile, mouse infection model experiment was also performed, showing a 5 log10 reduction of the pathogen after treatment with the resveratrol/POL combination. Moreover, adding adenosine disodium triphosphate (Na2ATP) could inhibit the antibacterial effect of the ATP synthase inhibitors/POL combination. In conclusion, our study confirmed that inhibition of ATP production could increase the susceptibility of bacteria carrying mcr-1 to polymyxins. This provides a new strategy against polymyxins-resistant bacteria infection.

lncRNAs as prognostic markers and therapeutic targets in cuproptosis-mediated cancer

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in various cellular processes, including cancer progression and stress response. Recent studies have demonstrated that copper accumulation induces a unique form of cell death known as cuproptosis, with lncRNAs playing a key role in regulating cuproptosis-associated pathways. These lncRNAs may trigger cell-specific responses to copper stress, presenting new opportunities as prognostic markers and therapeutic targets. This paper delves into the role of lncRNAs in cuproptosis-mediated cancer, underscoring their potential as biomarkers and targets for innovative therapeutic strategies. A thorough review of scientific literature was conducted, utilizing databases such as PubMed, Google Scholar, and ScienceDirect, with search terms like 'lncRNAs,' 'cuproptosis,' and 'cancer.' Studies were selected based on their relevance to lncRNA regulation of cuproptosis pathways and their implications for cancer prognosis and treatment. The review highlights the significant contribution of lncRNAs in regulating cuproptosis-related genes and pathways, impacting copper metabolism, mitochondrial stress responses, and apoptotic signaling. Specific lncRNAs are potential prognostic markers in breast, lung, liver, ovarian, pancreatic, and gastric cancers. The objective of this article is to explore the role of lncRNAs as potential prognostic markers and therapeutic targets in cancers mediated by cuproptosis.

Altered metabolism in cancer: insights into energy pathways and therapeutic targets

Cancer cells undergo significant metabolic reprogramming to support their rapid growth and survival. This study examines important metabolic pathways like glycolysis, oxidative phosphorylation, glutaminolysis, and lipid metabolism, focusing on how they are regulated and their contributions to the development of tumors. The interplay between oncogenes, tumor suppressors, epigenetic modifications, and the tumor microenvironment in modulating these pathways is examined. Furthermore, we discuss the therapeutic potential of targeting cancer metabolism, presenting inhibitors of glycolysis, glutaminolysis, the TCA cycle, fatty acid oxidation, LDH, and glucose transport, alongside emerging strategies targeting oxidative phosphorylation and lipid synthesis. Despite the promise, challenges such as metabolic plasticity and the need for combination therapies and robust biomarkers persist, underscoring the necessity for continued research in this dynamic field.

Multiobjective optimization of effect-directed planar chromatography as a promising tool for fast selection of polypotent natural products

A new method for efficiently selecting polypotent natural products is proposed in this study. The method involves using effect-directed HPTLC data and multiobjective optimization algorithms to extract chromatographic signals from HPTLC bioassay images. Three different multiobjective optimization methods, namely Derringer's desirability approach, Technique for order of preference by similarity to ideal solution (TOPSIS), and Sum of ranking differences (SRD), were applied to the chromatographic signals. In combination with jackknife cross-validation, Derringer's approach and TOPSIS demonstrated high similarity in finding the best (most polypotent), next to the best, next to the worst, and worst (least polypotent) extracts, while the SRD resulted in slightly different outcomes. Furthermore, a new method for identifying the chromatographic features that characterize the most polypotent extracts was proposed. This method is based on partial least square regression (PLS) and can be used in combination with HPTLC-chemical fingerprints to predict the desirability of new extracts. The resulting PLS models demonstrated high statistical performance with determination coefficients ranging from R2 = 0.885 in the case of Derringer's desirability, to 0.986 for TOPSIS. However, the PLS modeling of SRD values was not successful.

Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma

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.

Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma

Purpose

This study explores the potential of preclinical in vitro cell line response data and computational modeling in identifying 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 unique 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. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated in vivo translatability of in vitro dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed 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.

Synergistic action of antimicrobial peptides and antibiotics: current understanding and future directions

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.

Why Is Wnt/β-Catenin Not Yet Targeted in Routine Cancer Care?

Despite significant progress in cancer prevention, screening, and treatment, the still limited number of therapeutic options is an obstacle towards increasing the cancer cure rate. In recent years, many efforts were put forth to develop therapeutics that selectively target different components of the oncogenic Wnt/β-catenin signaling pathway. These include small molecule inhibitors, antibodies, and more recently, gene-based approaches. Although some of them showed promising outcomes in clinical trials, the Wnt/β-catenin pathway is still not targeted in routine clinical practice for cancer management. As for most anticancer treatments, a critical limitation to the use of Wnt/β-catenin inhibitors is their therapeutic index, i.e., the difficulty of combining effective anticancer activity with acceptable toxicity. Protecting healthy tissues from the effects of Wnt/β-catenin inhibitors is a major issue due to the vital role of the Wnt/β-catenin signaling pathway in adult tissue homeostasis and regeneration. In this review, we provide an up-to-date summary of clinical trials on Wnt/β-catenin pathway inhibitors, examine their anti-tumor activity and associated adverse events, and explore strategies under development to improve the benefit/risk profile of this therapeutic approach.

The new era of pancreatic cancer treatment: Application of nanotechnology breaking through bottlenecks

Since the advent of nanomedicine, physicians have harnessed these approaches for the prophylaxis, detection, and therapy of life-threatening diseases, particularly cancer. Nanoparticles have demonstrated notable efficacy in cancer therapy, showcasing the primary application of nanotechnology in targeted drug delivery. Pancreatic cancer stands out as the most lethal solid tumour in humans. The low survival rate is attributed to its highly aggressive nature, intrinsic resistance to chemotherapeutics, and the lack of successful therapies, compounded by delayed diagnosis due to nonspecific symptoms and the absence of rapid diagnostic strategies. Despite these challenges, nanotechnology-based carrier methods have been successfully employed in imaging and therapy approaches. Overcoming drug resistance in pancreatic cancer necessitates a comprehensive understanding of the microenvironment associated with the disease, paving the way for innovative nanocarriers. Hindered chemotherapy infiltration, attributed to inadequate vascularization and a dense tumour stroma, is a major hurdle that nanotechnology addresses. Intelligent delivery techniques, based on the Enhanced Permeability and Retention effect, form the basis of recently developed anticancer nanocarriers. These advancements aim to enhance drug accumulation in tumour locations, offering a potential solution to the treatment-resistant nature of cancer. Addressing the challenges in pancreatic cancer treatment demands innovative therapies, and the emergence of active nanocarriers presents a promising avenue for enhancing outcomes. This review specifically delves into the latest advancements in nanotechnology for the treatment of pancreatic cancer.

SRX3177, a CDK4/6-PI3K-BET inhibitor, in combination with an RdRp inhibitor, Molnupiravir, or an entry inhibitor MU-UNMC-2, has potent antiviral activity against the Omicron variant of SARS-CoV-2

Despite considerable progress in developing vaccines and antivirals to combat COVID-19, the rapid mutations of the SARS-CoV-2 genome have limited the durability and efficacy of the current vaccines and therapeutic interventions. Hence, it necessitates the development of novel therapeutic approaches or repurposing existing drugs that target either viral life cycle, host factors, or both. Here, we report that SRX3177, a potent triple-activity CDK4/6-PI3K-BET inhibitor, blocks replication of the SARS-CoV-2 Omicron variant with IC50 values at sub-micromolar concentrations without any impact on the cell proliferation of Calu-3 cells at and below its IC50 concentration. When SRX3177 is combined with EIDD-1931 (active moiety of a small-molecule prodrug Molnupiravir) or MU-UNMC-2 (a SARS-CoV-2 entry inhibitor) at a fixed doses matrix, a synergistic effect was observed, leading to the significant reduction in the dose of the individual compounds to achieve similar inhibition of SARS-CoV-2 replication. Herein, we report that the combination of SRX3177/MPV or SRX3177/UM-UNMC-2 has the potential for further development as a combinational therapy against SARS-CoV-2 and in any future outbreak of beta coronavirus.

Advancements in Therapeutic Approaches for Proliferative Vitreoretinopathy: A Comprehensive Review

Proliferative vitreoretinopathy (PVR) is a significant complication of retinal detachment surgery, characterized by the growth of fibrous membranes that can lead to recurrent retinal detachment and vision loss. This comprehensive review aims to summarize the latest advancements in the therapeutic approaches for PVR, encompassing historical perspectives, current surgical techniques, pharmacological interventions, biological and genetic therapies, and novel experimental treatments. Traditional surgical methods, such as vitrectomy, have been refined with advanced instrumentation and techniques to improve outcomes. Pharmacological treatments, including anti-inflammatory and anti-proliferative agents, are being explored to prevent and manage PVR. Emerging therapies, such as stem cell and gene therapy, offer promising new avenues for treatment. Despite these advancements, challenges remain in preventing recurrence and improving long-term outcomes. This review highlights the progress made and identifies areas for future research, emphasizing the importance of continued innovation to enhance patient care and reduce the burden of PVR.

Engineering nanoparticles-enabled tumor-associated macrophages repolarization and phagocytosis restoration for enhanced cancer immunotherapy

Tumor-associated macrophages (TAMs) are pivotal within the immunosuppressive tumor microenvironment (TME), and recently, have attracted intensive attention for cancer treatment. However, concurrently to promote TAMs repolarization and phagocytosis of cancer cells remains challenging. Here, a TAMs-targeted albumin nanoparticles-based delivery system (M@SINPs) was constructed for the co-delivery of photosensitizer IR820 and SHP2 inhibitor SHP099 to potentiate macrophage-mediated cancer immunotherapy. M@SINPs under laser irradiation can generate the intracellular reactive oxygen species (ROS) and facilitate M2-TAMs to an M1 phenotype. Meanwhile, inhibition of SHP2 could block the CD47-SIRPa pathway to restore M1 macrophage phagocytic activity. M@SINPs-mediated TAMs remodeling resulted in the immunostimulatory TME by repolarizing TAMs to an M1 phenotype, restoring its phagocytic function and facilitating intratumoral CTLs infiltration, which significantly inhibited tumor growth. Furthermore, M@SINPs in combination with anti-PD-1 antibody could also improve the treatment outcomes of PD-1 blockade and exert the synergistic anticancer effects. Thus, the macrophage repolarization/phagocytosis restoration combination through M@SINPs holds promise as a strategy to concurrently remodel TAMs in TME for improving the antitumor efficiency of immune checkpoint block and conventional therapy.

Reduction of H3K9 methylation by G9a inhibitors improves the development of mouse SCNT embryos

Removal of somatic histone H3 lysine 9 trimethylation (H3K9me3) from the embryonic genome can improve the efficiency of mammalian cloning using somatic cell nuclear transfer (SCNT). However, this strategy involves the injection of histone demethylase mRNA into embryos, which is limiting because of its invasive and labor-consuming nature. Here, we report that treatment with an inhibitor of G9a (G9ai), the major histone methyltransferase that introduces H3K9me1/2 in mammals, greatly improved the development of mouse SCNT embryos. Intriguingly, G9ai caused an immediate reduction of H3K9me1/2, a secondary loss of H3K9me3 in SCNT embryos, and increased the birth rate of cloned pups about 5-fold (up to 3.9%). G9ai combined with the histone deacetylase inhibitor trichostatin A further improved this rate to 14.5%. Mechanistically, G9ai and TSA synergistically enhanced H3K9me3 demethylation and boosted zygotic genome activation. Thus, we established an easy, highly effective SCNT protocol that would enhance future cloning research and applications.

Evolving Strategies for Use of Phytochemicals in Prevention and Long-Term Management of Cardiovascular Diseases (CVD)

This report describes major pathomechanisms of disease in which the dysregulation of host inflammatory processes is a major factor, with cardiovascular disease (CVD) as a primary model, and reviews strategies for countermeasures based on synergistic interaction between various agents, including drugs and generally regarded as safe (GRAS) natural medical material (NMM), such as Ginkgo biloba, spice phytochemicals, and fruit seed flavonoids. The 15 well-defined CVD classes are explored with particular emphasis on the extent to which oxidative stressors and associated ischemia-reperfusion tissue injury contribute to major symptoms. The four major categories of pharmaceutical agents used for the prevention of and therapy for CVD: statins, beta blockers (β-blockers), blood thinners (anticoagulants), and aspirin, are presented along with their adverse effects. Analyses of major cellular and molecular features of drug- and NMM-mediated cardioprotective processes are provided in the context of their development for human clinical application. Future directions of the evolving research described here will be particularly focused on the characterization and manipulation of calcium- and calcineurin-mediated cascades of signaling from cell surface receptors on cardiovascular and immune cells to the nucleus, with the emergence of both protective and pathological epigenetic features that may be modulated by synergistically-acting combinations of drugs and phytochemicals in which phytochemicals interact with cells to promote signaling that reduces the effective dosage and thus (often) toxicity of drugs.

SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations

The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficulty of prediction. To address this challenge, we propose a simple machine learning framework, namely Similarity Network-based Synergy prediction (SNSynergy), for predicting synergistic effects towards new cell lines and new drug combinations by two locally weighted models CLSN and DCSN. This framework only requires a small amount of auxiliary data, like genomics information of cell lines and the molecular fingerprints or targets of drugs. Based on the assumption that similar cell lines and similar drug combinations have similar synergistic effects, CLSN and DCSN predict synergy scores through capturing individual synergy contributions of nearest cell line and drug combination neighbors, respectively. High correlations between predicted and measured synergy scores on two leading cancer cell line pharmacogenomic screening datasets (the O'Neil dataset and the NCI-ALMANAC dataset) demonstrate the effectiveness and robustness of SNSynergy. Many of the identified drug combinations are consistent with previous studies, or have been explored in clinical settings against the specific cancer type, showing that SNSynergy has the potential to supply cost-saving and effective high-throughput screening for prioritizing the most applicable cell lines and the most promising drug combinations.

N-Benzylated 5-Hydroxybenzothiophene-2-carboxamides as Multi-Targeted Clk/Dyrk Inhibitors and Potential Anticancer Agents

Numerous studies have reported that Dyrk1A, Dyrk1B, and Clk1 are overexpressed in multiple cancers, suggesting a role in malignant disease. Here, we introduce a novel class of group-selective kinase inhibitors targeting Dyrk1A, Dyrk1B, and Clk1. This was achieved by modifying our earlier selective Clk1 inhibitors, which were based on the 5-methoxybenzothiophene-2-carboxamide scaffold. By incorporating a 5-hydroxy group, we increased the potential for additional hydrogen bond interactions that broadened the inhibitory effect to include Dyrk1A and Dyrk1B kinases. Within this series, compounds 12 and 17 emerged as the most potent multi-kinase inhibitors against Dyrk1A, Dyrk1B, and Clk1. Furthermore, when assessed against the most closely related kinases also implicated in cancer, the frontrunner compounds revealed additional inhibitory activity against Haspin and Clk2. Compounds 12 and 17 displayed high potency across various cancer cell lines with minimal effect on non-tumor cells. By examining the effect of these inhibitors on cell cycle distribution, compound 17 retained cells in the G2/M phase and induced apoptosis. Compounds 12 and 17 could also increase levels of cleaved caspase-3 and Bax, while decreasing the expression of the antiapoptotic Bcl-2 protein. These findings support the further study and development of these compounds as novel anticancer therapeutics.

Current and emerging strategies to curb antibiotic-resistant urinary tract infections

Rising rates of antibiotic resistance in uropathogenic bacteria compromise patient outcomes and prolong hospital stays. Consequently, new strategies are needed to prevent and control the spread of antibiotic resistance in uropathogenic bacteria. Over the past two decades, sizeable clinical efforts and research advances have changed urinary tract infection (UTI) treatment and prevention strategies to conserve antibiotic use. The emergence of antimicrobial stewardship, policies from national societies, and the development of new antimicrobials have shaped modern UTI practices. Future UTI management practices could be driven by the evolution of antimicrobial stewardship, improved and readily available diagnostics, and an improved understanding of how the microbiome affects UTI. Forthcoming UTI treatment and prevention strategies could employ novel bactericidal compounds, combinations of new and classic antimicrobials that enhance bacterial killing, medications that prevent bacterial attachment to uroepithelial cells, repurposing drugs, and vaccines to curtail the rising rates of antibiotic resistance in uropathogenic bacteria and improve outcomes in people with UTI.

DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile

Great efforts have been made over the years to identify novel drug pairs with synergistic effects. Although numerous computational approaches have been proposed to analyze diverse types of biological big data, the pharmacogenomic profiles, presumably the most direct proxy of drug effects, have been rarely used due to the data sparsity problem. In this study, we developed a composite deep-learning-based model that predicts the drug synergy effect utilizing pharmacogenomic profiles as well as molecular properties. Graph convolutional network (GCN) was used to represent and integrate the chemical structure, genetic interactions, drug-target information, and gene expression profiles of cell lines. Insufficient amount of pharmacogenomic data, i.e., drug-induced expression profiles from the LINCS project, was resolved by augmenting the data with the predicted profiles. Our method learned and predicted the Loewe synergy score in the DrugComb database and achieved a better or comparable performance compared to other published methods in a benchmark test. We also investigated contribution of various input features, which highlighted the value of basal gene expression and pharmacogenomic profiles of each cell line. Importantly, DRSPRING (DRug Synergy PRediction by INtegrated GCN) can be applied to any drug pairs and any cell lines, greatly expanding its applicability compared to previous methods.

(2,6-Dimethylphenyl)arsonic Acid Induces Apoptosis through the Mitochondrial Pathway, Downregulates XIAP, and Overcomes Multidrug Resistance to Cytostatic Drugs in Leukemia and Lymphoma Cells In Vitro

Cancer treatment is greatly challenged by drug resistance, highlighting the need for novel drug discoveries. Here, we investigated novel organoarsenic compounds regarding their resistance-breaking and apoptosis-inducing properties in leukemia and lymphoma. Notably, the compound (2,6-dimethylphenyl)arsonic acid (As2) demonstrated significant inhibition of cell proliferation and induction of apoptosis in leukemia and lymphoma cells while sparing healthy leukocytes. As2 reached half of its maximum activity (AC50) against leukemia cells at around 6.3 µM. Further experiments showed that As2 overcomes multidrug resistance and sensitizes drug-resistant leukemia and lymphoma cell lines to treatments with the common cytostatic drugs vincristine, daunorubicin, and cytarabine at low micromolar concentrations. Mechanistic investigations of As2-mediated apoptosis involving FADD (FAS-associated death domain)-deficient or Smac (second mitochondria-derived activator of caspases)/DIABLO (direct IAP binding protein with low pI)-overexpressing cell lines, western blot analysis of caspase-9 cleavage, and measurements of mitochondrial membrane integrity identified the mitochondrial apoptosis pathway as the main mode of action. Downregulation of XIAP (x-linked inhibitor of apoptosis protein) and apoptosis induction independent of Bcl-2 (B-cell lymphoma 2) and caspase-3 expression levels suggest the activation of additional apoptosis-promoting mechanisms. Due to the selective apoptosis induction, the synergistic effects with common anti-cancer drugs, and the ability to overcome multidrug resistance in vitro, As2 represents a promising candidate for further preclinical investigations with respect to refractory malignancies.

Full-Combinatorial Concentration Gradient Array with 3D Micro/Nanofluidics for Antibiotic Susceptibility Testing

Recent advancements in micro/nanofluidics have facilitated on-chip microscopy of cellular responses in a high-throughput and controlled microenvironment with the desired physicochemical properties. Despite its potential benefits to combination drug discovery, generating a complete combinatorial set of concentration gradients for multiple reagents in an array format remains challenging. The main reason is limited layouts of conventional micro/nanofluidic systems based on two-dimensional channel networks. In this paper, we present a device with three-dimensional (3D) interconnection of micro/nanochannels capable of generating a complete combinatorial set of concentration gradients for two reagents. The device was readily fabricated by laminating a pair of multilayered monolithic films containing a Christmas tree-like mixer, a cell culture chamber array, and through-holes, all within each single film. We assessed the reliable generation of a full-combinatorial concentration gradient array and validated it by using numerical analysis. We applied the proposed device to test the antibiotic susceptibility of bacterial cells in a convenient one-step manner. Furthermore, we explored the potential of the device to accommodate the arrayed complete combinatorial set for two or more drugs, while extending the capabilities of our laminated object manufacturing method for realizing 3D micro/nanofluidic systems.

Optimization of Epigenetic Modifier Drug Combination for Synergistic Effect against Glioblastoma Multiform Cancer Cell Lines

Glioblastoma multiforme (GBM), is a frequent class of malignant brain tumors. Epigenetic therapy, especially with synergistic combinations is highly paid attention for aggressive solid tumors like GBM. Here, RSM optimization has been used to increase the efficient arrest of U87 and U251 cell lines due to synergistic effects. Cell lines were treated with SAHA, 5-Azacytidine, GSK-126, and PTC-209 individually and then RSM was used to find most effective combinations. Results showed that optimized combinations significantly reduce cell survival and induce cell cycle arrest and apoptosis in both cell lines. Expression of cyclin B1 and cyclin D1 were decreased while caspase3 increased expression.

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/.

Engineered cell membrane-coated nanoparticles based cancer therapy: A robust weapon against the lethal and challenging hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has become an important public health problem, and there are still challenges to overcome in clinical treatment. The nanodrug delivery system (NDDS) has developed tremendously in recent years, and many researchers have explored NDDS for the treatment of HCC. Engineered cell membrane-coated nanoparticles (ECNPs) have emerged, combining the unique functions of cell membranes with the engineering versatility of synthetic nanoparticles (NPs) to effectively deliver therapeutic drugs. It is designed to have the capabilities: specific active targeting, immune evasion, prolonging the circulation blood time, controlled drug release delivery, and reducing drugs systematic toxicity. Thus, ECNPs are a promising bionic tool in the treatment of HCC and have operability to achieve combination and integrated therapy. This review focuses on the mechanism and strategy of ECNPs for the treatment of HCC and summarizes its research progress in the treatment of HCC in recent years.

The Conjugates of Indolo[2,3-b]quinoline as Anti-Pancreatic Cancer Agents: Design, Synthesis, Molecular Docking and Biological Evaluations

New amide conjugates of hydroxycinnamic acids (HCAs) and the known antineoplastic 5,11-dimethyl-5H-indolo[2,3-b]quinoline (DiMIQ), an analog of the natural alkaloid neocryptolepine, were synthesized and tested in vitro for anticancer activity. The compound 9-[((2-hydroxy)cinnamoyl)amino]-5,11-dimethyl-5H-indolo[2,3-b]quinoline (2), which contains the ortho-coumaric acid fragment, demonstrated dose-dependent effectiveness against both normal BxPC-3 and metastatic AsPC-1 pancreatic cancer cells. The IC50 values for AsPC-1 and BxPC-3 were 336.5 nM and 347.5 nM, respectively, with a selectivity index of approximately 5 for both pancreatic cancer cells compared to normal dermal fibroblasts. Conjugate 2 did not exhibit any hemolytic activity against human erythrocytes at the tested concentration. Computational studies were performed to predict the pharmacokinetic profile and potential mechanism of action of the synthesized conjugates. These studies focused on the ADME properties of the conjugates and their interactions with DNA, as well as DNA-topoisomerase alpha and beta complexes. All of the conjugates studied showed approximately one order of magnitude stronger binding to DNA compared to the reference DiMIQ, and approximately two orders of magnitude stronger binding to the topoisomerase II-DNA complex compared to DiMIQ. Conjugate 2 was predicted to have the strongest binding to the enzyme-DNA complex, with a Ki value of 2.8 nM.

Protective Effect of the Naringin-Chitooligosaccharide Complex on Lipopolysaccharide-Induced Systematic Inflammatory Response Syndrome Model in Mice

Naringin is one of the common flavonoids in grapefruit, which has anti-cancer, antioxidant, and anti-inflammatory activities. However, its poor solubility limits its wide application. Therefore, the aim of this study is to investigate the anti-inflammatory effect of naringin combined with chitooligosaccharides with good biocompatibility by constructing a mouse model of systemic inflammatory response syndrome (SIRS). The results showed that the naringin-chitooligosaccharide (NG-COS) complex significantly inhibited lipopolysaccharide (LPS)-induced weight loss, reduced food intake, tissue inflammatory infiltration, and proinflammatory cytokines IL-6, TNF-α, INF-γ, and IL-1β levels. The complex also significantly affected the content of malondialdehyde and the activities of MPO, SOD, and GSH in the liver, spleen, lungs, and serum of mice with systemic inflammation. In addition, NG-COS significantly inhibited the mRNA expression of inflammatory factors in the TLR4/NF-κB signaling pathway. Principal component analysis showed that the complexes could inhibit LPS-induced systemic inflammation in mice, and the effect was significantly better than that of naringin and chitooligosaccharides alone. This study explored the synergistic effects of chitosan and naringin in reducing inflammation and could contribute to the development of novel biomedical interventions.

Targeting glycogen synthase kinase-3β for Alzheimer's disease: Recent advances and future Prospects

Senile plaques induced by β-amyloid (Aβ) abnormal aggregation and neurofibrillary tangles (NFT) caused by tau hyperphosphorylation are important pathological manifestations of Alzheimer's disease (AD). Glycogen synthase kinase-3 (GSK-3) is a conserved kinase; one member GSK-3β is highly expressed in the AD brain and involved in the formation of NFT. Hence, pharmacologically inhibiting GSK-3β activity and expression is a good approach to treat AD. As summarized in this article, multiple GSK-3β inhibitors has been comprehensively summarized over recent five years. However, only lithium carbonate and Tideglusib have been studied in clinical trials of AD. Besides ATP-competitive and non-ATP-competitive inhibitors, peptide inhibitors, allosteric inhibitors and other types of inhibitors have gradually attracted more interest. Moreover, considering the close relationship between GSK-3β and other targets involved in cholinergic hypothesis, Aβ aggregation hypothesis, tau hyperphosphorylation hypothesis, oxidative stress hypothesis, neuro-inflammation hypothesis, etc., diverse multifunctional molecules and multi-target directed ligands (MTDLs) have also been disclosed. We hope that these recent advances and critical perspectives will facilitate the discovery of safe and effective GSK-3β inhibitors for AD treatment.

Network-based drug repurposing identifies small molecule drugs as immune checkpoint inhibitors for endometrial cancer

Endometrial cancer (EC) is the 6th most common cancer in women around the world. Alone in the United States (US), 66,200 new cases and 13,030 deaths are expected to occur in 2023 which needs the rapid development of potential therapies against EC. Here, a network-based drug-repurposing strategy is developed which led to the identification of 16 FDA-approved drugs potentially repurposable for EC as potential immune checkpoint inhibitors (ICIs). A network of EC-associated immune checkpoint proteins (ICPs)-induced protein interactions (P-ICP) was constructed. As a result of network analysis of P-ICP, top key target genes closely interacting with ICPs were shortlisted followed by network proximity analysis in drug-target interaction (DTI) network and pathway cross-examination which identified 115 distinct pathways of approved drugs as potential immune checkpoint inhibitors. The presented approach predicted 16 drugs to target EC-associated ICPs-induced pathways, three of which have already been used for EC and six of them possess immunomodulatory properties providing evidence of the validity of the strategy. Classification of the predicted pathways indicated that 15 drugs can be divided into two distinct pathway groups, containing 17 immune pathways and 98 metabolic pathways. In addition, drug-drug correlation analysis provided insight into finding useful drug combinations. This fair and verified analysis creates new opportunities for the quick repurposing of FDA-approved medications in clinical trials.

Synergistic potential of teriflunomide with fluconazole against resistant Candida albicans in vitro and in vivo

Introduction

Candida albicans is the primary cause of systemic candidiasis, which is involved in high morbidity and mortality. Drug resistance exacerbates these problems. In addition, there are limited antifungal drugs available. In order to solve these problems, combination therapy has aroused great interest. Teriflunomide is an immunosuppressant. In the present work, we aimed to identify whether teriflunomide can reverse the resistance of Candida albicans in the presence of sub-inhibitory concentrations of fluconazole in vitro and in vivo.

Methods

Seven Candida albicans isolates were used in this study. Susceptibility of Candida albicans in vitro to the drugs was determined using a checkerboard microdilution assay in accordance with the recommendations of the Clinical and Laboratory Standards Institute. The effects of drugs on biofilm biomass of Candida albicans were determined by crystal violet staining. The development ability of Candida albicans hyphae was performed using a modified broth microdilution method. Galleria mellonella was used for testing the in vivo efficacy of the combination therapies.

Results

We found that the combination of teriflunomide (64 µg/mL) and fluconazole (0.5-1 µg/mL) has a significant synergistic effect in all resistant Candida albicans isolates (n=4). Also, this drug combination could inhibit the immature biofilm biomass and hyphae formation of resistant Candida albicans. Galleria mellonella was used for testing the in vivo efficacy of this combination therapies. As for the Galleria mellonella larvae infected by resistant Candida albicans, teriflunomide (1.6 µg/larvae) combined with fluconazole (1.6 µg/larvae) significantly increased their survival rates, and reduced the fungal burden, as well as damage of tissue in comparison to that in the control group or drug monotherapy group.

Conclusion

These results expand our knowledge about the antifungal potential of teriflunomide as an adjuvant of existing antifungal drugs, and also open new perspectives in the treatment of resistant Candida albicans based on repurposing clinically available nonantifungal drugs.

An AI Approach to Identifying Novel Therapeutics for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has a significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain; however, achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches for novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offer the ability to repurpose FDA-approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed, including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations and the ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms that have strong potential to determine novel and effective treatment regimens.

MM-GANN-DDI: Multimodal Graph-Agnostic Neural Networks for Predicting Drug-Drug Interaction Events

Personalized treatment of complex diseases relies on combined medication. However, the occurrence of unexpected drug-drug interactions (DDIs) in these combinations can lead to adverse effects or even fatalities. Although recent computational methods exhibit promising performance in DDI screening, their practical implementation faces two significant challenges: (i) the availability of comprehensive datasets to support clinical application, and (ii) the ability to infer DDI types for new drugs beyond the existing dataset coverage. To mitigate these challenges, we propose MM-GANN-DDI: a Multimodal Graph-Agnostic Neural Network for Predicting Drug-Drug Interaction Events. We first mine six drug modalities and incorporate a graph attention (GAT) mechanism to fuse these modalities with the topological features of the DDI graph. We further propose a novel graph neural network training mechanism called graph-agnostic meta-training (GAMT), which effectively leverages topological information from the DDI graph and efficiently predicts DDI types for new drugs beyond the available dataset. Specifically, GAMT samples meta-graphs from the original DDI graph, splitting them into support and query sets to simulate seen and unseen drugs. Two-level optimizations are applied to enhance the model's generalization capability. We evaluate our model on two datasets (DB-v1 and DB-v2) across three tasks. Our MM-GANN-DDI demonstrates competitive performance on all three tasks. Notably, in Task 2, which focuses on predicting DDI types for drugs outside the dataset, our proposed model outperforms other methods, exhibiting an improvement of 4.6 percentage points in AUPR on DB-v1 and 5.9 percentage points on DB-v2. Additionally, our model surpasses state-of-the-art methods and classic approaches in terms of accuracy, F1 score, precision, and recall. Ablation experiments provide further validation of the effectiveness of the proposed model design. Importantly, our model exhibits the potential to discover unobserved DDIs, demonstrating its practical application in clinical medication.

Lipid-based nanoparticle-mediated combination therapy for breast cancer management: a comprehensive review

Breast cancer due to the unpredictable and complex etiopathology combined with the non-availability of any effective drug treatment has become the major root of concern for oncologists globally. The number of women affected by the said disease state is increasing at an alarming rate attributed to environmental and lifestyle changes indicating at the exploration of a novel treatment strategy that can eradicate this aggressive disease. So far, it is treated by promising nanomedicine monotherapy; however, according to the numerous studies conducted, the inadequacy of these nano monotherapies in terms of elevated toxicity and resistance has been reported. This review, therefore, puts forth a new multimodal strategic approach to lipid-based nanoparticle-mediated combination drug delivery in breast cancer, emphasizing the recent advancements. A basic overview about the combination therapy and its index is firstly given. Then, the various nano-based combinations of chemotherapeutics involving the combination delivery of synthetic and herbal agents are discussed along with their examples. Further, the recent exploration of chemotherapeutics co-delivery with small interfering RNA (siRNA) agents has also been explained herein. Finally, a section providing a brief description of the delivery of chemotherapeutic agents with monoclonal antibodies (mAbs) has been presented. From this review, we aim to provide the researchers with deep insight into the novel and much more effective combinational lipid-based nanoparticle-mediated nanomedicines tailored specifically for breast cancer treatment resulting in synergism, enhanced antitumor efficacy, and low toxic effects, subsequently overcoming the hurdles associated with conventional chemotherapy.

Survivin-targeted nanomedicine for increased potency of abiraterone and enzalutamide against prostate cancer

Prostate cancer is the leading and most aggressive cancer around the world, several therapeutic approaches have emerged but none have achieved the satisfactory result. However, these therapeutic approaches face many challenges related to their delivery to target cells, including their in vivo decay, the limited uptake by target cells, the requirements for nuclear penetration (in some cases), and the damage caused to healthy cells. These barriers can be avoided by effective, targeted, combinatorial approaches, with minimal side effects, which are being investigated for the treatment of cancer. Here, we developed a combinatorial nanomedicine comprising abiraterone and enzalutamide bioconjugated survivin-encapsulated gold nanoparticles (AbEzSvGNPs) for targeted therapy of prostate cancer. AbEzSvGNPs were characterized by different biophysical techniques such as UV visible spectroscopy, dynamic light scattering, zeta potential, transmission electron microscope, and Fourier transform infrared spectroscopy. Interestingly, the effect of abiraterone, enzalutamide and surviving encapsulated gold nanoparticles was found to be synergistic in nature in AbEzSvGNPs against DU 145 (IC50 = 4.21 µM) and PC-3 (IC50 = 5.58 µM) cells and their potential was observed to be greatly enhanced as compared with the combined effect of the drugs (abiraterone and enzalutamide) in their free form. Furthermore, AbEzSvGNPs were found to be highly safe and did not exhibit significant cytotoxicity against normal rat kidney cells. The observed effects of AbEzSvGNPs involved the modulation of different signaling pathways in prostate cancer cells. This delivery system employed non-androgen receptor-dependent delivery of abiraterone and enzalutamide. The anionic AbEzSvGNPs delivered abiraterone and enzalutamide unaltered into the nucleus through caveolae mediated internalization to act nonspecifically on DNA; internalization of the anionic nanoparticles into the cytoplasm was also observed via other routes. AbEzSvGNPs synthesized and evaluated in this study are promising candidates for prostate cancer therapy.

In Vitro and In Vivo Antimicrobial Activity of an Oxidative Stress-Mediated Bicyclic Menaquinone Biosynthesis Inhibitor against MRSA

Menaquinone (MK) is an essential component in the oxidative phosphorylation pathway of Gram-positive bacteria. Drugs targeting enzymes involved in MK biosynthesis can prevent electron transfer, which leads to ATP starvation and thereby death of microorganisms. Previously, we reported a series of MenA inhibitors and demonstrated their antimicrobial activity against Gram-positive bacteria, including Methicillin-resistant Staphylococcus aureus (MRSA) and mycobacteria. These inhibitors were developed by mimicking demethylmenaquinone, a product of MenA enzymatic reaction in MK biosynthesis. In this study, compound NM4, MK biosynthesis inhibitor, inhibited the formation of MRSA biofilm and it was screened against 1952 transposon mutants to elucidate mechanisms of action; however, no resistant mutants were found. Also, compound NM4 induced the production of reactive oxygen species (ROS) by blocking electron transfer in the oxidative phosphorylation pathway as observed by MRSA growth recovery using various ROS scavengers. An oxygen consumption assay also showed that NM4 blocks the oxygen consumption by MRSA, but the addition of menaquinone (MK) restores growth of MRSA. The NM4-treated MRSA induced the expression of catalase by more than 25%, as quantified by the native gel. A pulmonary murine model exhibited that NM4 significantly reduced bacterial lung load in mice without toxicity. An NM4-resistant USA300 strain was developed to attempt to identify the targets participating in the mechanism of resistance. Our results support that respiration and oxidative phosphorylation are potential targets for developing antimicrobial agents against MRSA. Altogether, our findings suggest the potential use of MK biosynthesis inhibitors as an effective antimicrobial agent against MRSA.

The synthesis, thermal behaviour, spectral and structural characterization, and in silico prediction of pharmacokinetic parameters of tetraalkylammonium salts of non-steroidal anti-inflammatory drug nimesulide

The synthesis, spectral properties, thermal analysis, structural characterization and in silico prediction of pharmacokinetic parameters of tetramethylammonium (compound 1) and tetraethylammonium (compound 2) salt of nimesulide were described in this article. Both compounds crystallize in the monoclinic P21/n space group, with one tetraalkylammonium cation and one nimesulide anion in the asymmetric unit and their crystal structures are stabilized by C-H···O hydrogen bonds between ions. Additionally, structures of title compounds are stabilized by π-π interactions (compound 1), or C-H···π interactions (compound 2) between nimesulide anions. The TG and DSC measurements show that compound 1 melts at a temperature higher than nimesulide, whereas the compound 2 melts at a temperature lower than nimesulide. The MALDI-TOF, 1H NMR, 13C NMR and ATR-FTIR analyses confirm the SCXRD study, that in compounds 1 and 2 nimesulide exists in an ionized form. Studies performed by SWISS ADME and ProTOX II tools, predict to be oral bioavailability of both salts obtained, and one of them (compound 1) is predicted to be well-absorbed by digestive system, while both compounds obtained are classified into toxicity class 4.

Nano-drug delivery system targeting tumor microenvironment: A prospective strategy for melanoma treatment

Melanoma, the most aggressive form of cutaneous malignancy arising from melanocytes, is frequently characterized by metastasis. Despite considerable progress in melanoma therapies, patients with advanced-stage disease often have a poor prognosis due to the limited efficacy, off-target effects, and toxicity associated with conventional drugs. Nanotechnology has emerged as a promising approach to address these challenges with nanoparticles capable of delivering therapeutic agents specifically to the tumor microenvironment (TME). However, the clinical approval of nanomedicines for melanoma treatment remains limited, necessitating further research to develop nanoparticles with improved biocompatibility and precise targeting capabilities. This comprehensive review provides an overview of the current research on nano-drug delivery systems for melanoma treatment, focusing on liposomes, polymeric nanoparticles, and inorganic nanoparticles. It discusses the potential of these nanoparticles for targeted drug delivery, as well as their ability to enhance the efficacy of conventional drugs while minimizing toxicity. Furthermore, this review emphasizes the significance of interdisciplinary collaboration between researchers from various fields to advance the development of nanomedicines. Overall, this review serves as a valuable resource for researchers and clinicians interested in the potential of nano-drug delivery systems for melanoma treatment and offers insights into future directions for research in this field.

Tamoxifen-Loaded Eudragit Nanoparticles: Quality by Design Approach for Optimization of Nanoparticles as Delivery System

Nanoparticles have numerous applications as drug carriers in drug delivery. The aim of the study was to produce tamoxifen nanoparticles with a defined size and higher encapsulation for efficient tissue uptake with controlled drug release. The quality by design approach was utilized to produce tamoxifen-loaded Eudragit nanoparticles by identifying the significant process variables using the nanoprecipitation method. The process variables (amount of drug, polymer, and surfactant) were altered to analyze the influence on particle size (PS), % encapsulation efficiency (EE). The results showed that the drug and polymer individually as well as collectively have an impact on PS, while the surfactant has no impact on the PS. The %EE was influenced by the surfactant individually and in interaction with the drug. The linear regression model was endorsed to fit the data showing high R2 values (PS, 0.9146, %EE, 0.9070) and low p values (PS, 0.0004, EE, 0.0005). The PS and EE were confirmed to be 178 nm and 90%, respectively. The nanoparticles were of spherical shape, as confirmed by SEM and TEM. The FTIR confirmed the absence of any incompatibility among the ingredients. The TGA confirmed that the NPs were thermally stable. The in vitro release predicted that the drug release followed Higuchi model.

Melatonin targets MoIcl1 and works synergistically with fungicide isoprothiolane in rice blast control

Melatonina natural harmless molecule-displays versatile roles in human health and crop disease control such as for rice blast. Rice blast, caused by the filamentous fungus Magnaporthe oryzae, is one devastating disease of rice. Application of fungicides is one of the major measures in the control of various crop diseases. However, fungicide resistance in the pathogen and relevant environmental pollution are becoming serious problems. By screening for possible synergistic combinations, here, we discovered an eco-friendly combination for rice blast control, melatonin, and the fungicide isoprothiolane. These compounds together exhibited significant synergistic inhibitory effects on vegetative growth, conidial germination, appressorium formation, penetration, and plant infection by M. oryzae. The combination of melatonin and isoprothiolane reduced the effective concentration of isoprothiolane by over 10-fold as well as residual levels of isoprothiolane. Transcriptomics and lipidomics revealed that melatonin and isoprothiolane synergistically interfered with lipid metabolism by regulating many common targets, including the predicted isocitrate lyase-encoding gene MoICL1. Furthermore, using different techniques, we show that melatonin and isoprothiolane interact with MoIcl1. This study demonstrates that melatonin and isoprothiolane function synergistically and can be used to reduce the dosage and residual level of isoprothiolane, potentially contributing to the environment-friendly and sustainable control of crop diseases.

An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures

Drug resistance currently poses the greatest barrier to cancer treatments. To overcome drug resistance, drug combination therapy has been proposed as a promising treatment strategy. Herein, we present Re-Sensitizing Drug Prediction (RSDP), a novel computational strategy, for predicting the personalized cancer drug combination A + B by reversing the resistance signature of drug A. The process integrates multiple biological features using a robust rank aggregation algorithm, including Connectivity Map, synthetic lethality, synthetic rescue, pathway, and drug target. Bioinformatics assessments revealed that RSDP achieved a relatively accurate prediction performance for identifying personalized combinational re-sensitizing drug B against cell line-specific intrinsic resistance, cell line-specific acquired resistance, and patient-specific intrinsic resistance to drug A. In addition, we developed the largest resource of cell line-specific cancer drug resistance signatures, including intrinsic and acquired resistance, as a byproduct of the proposed strategy. The findings indicate that personalized drug resistance signature reversal is a promising strategy for identifying personalized drug combinations, which may guide future clinical decisions regarding personalized medicine.

The effect of protocatechuic acid on neuropathic pain and possible mechanism

OBJECTIVES

The goal of the research is to investigate the protocatechuic acid (PCA) potential action, a phenolic acid derivative, on pain induced by neuropathy and to determine its efficacy on activation of KATP type channels and A1 receptors.

MATERIALS AND METHODS

Neuropathic pain by cause of sciatic nerve damage was induced in Sprague-Dawley rats. Anti-allodynic and anti-hyperalgesic effects were evaluated with von Frey apparatus and Hargreave's plantar test apparatus, respectively. The effects of PCA at the doses of 75, 150 and 300 mg/kg, carbamazepine at the doses of 50 and 100 mg/kg, combination of low effective doses of PCA and carbamazepine were tested. Pretreatments 3 μg/kg DPCPX as adenosine A1 receptor antagonist and 60.7 nmol glibenclamide as KATP channel blocker were applied for mechanistic studies.

RESULTS

PCA showed anti-allodynic and anti-hyperalgesic effects without impairing locomotor activity. In addition, the combination treatment was found to be more effective than the separate individual treatments of drugs. KATP channel activation related with A1 receptor stimulation makes a significant contribution to the anti-allodynia and anti-hyperalgesia induced by PCA.

CONCLUSIONS

It can be said that PCA has similar effects with carbamazepine, which is used in clinical practice, and that PCA can take place as an adjuvant drug in neuropathic pain with the combination group. In addition, it is seen that the undesirable effects that drugs can cause alone can be avoided and a more effective treatment potential can be created with multiple mechanisms.

Ratiometric codelivery of Paclitaxel and Baicalein loaded nanoemulsion for enhancement of breast cancer treatment

The most prevalent clinical option for treating cancer is combination chemotherapy. In combination therapy, assessment and optimization for obtaining a synergistic ratio could be obtained by various preclinical setups. Currently, in vitro optimization is used to get synergistic cytotoxicity while constructing combinations. Herein, we co-encapsulated Paclitaxel (PTX) and Baicalein (BCLN) with TPP-TPGS1000 containing nanoemulsion (TPP-TPGS1000-PTX-BCLN-NE) for breast cancer treatment. The assessment of cytotoxicity of PTX and BCLN at different molar weight ratios provided an optimized synergistic ratio (1:5). Quality by Design (QbD) approach was later applied for the optimization as well as characterization of nanoformulation for its droplet size, zeta potential and drug content. TPP-TPGS1000-PTX-BCLN-NE significantly enhanced cellular ROS, cell cycle arrest, and depolarization of mitochondrial membrane potential in the 4T1 breast cancer cell line compared to other treatments. In the syngeneic 4T1 BALB/c tumor model, TPP-TPGS1000-PTX-BCLN-NE outperformed other nanoformulation treatments. The pharmacokinetic, biodistribution and live imaging studies pivoted TPP-TPGS1000-PTX-BCLN-NE enhanced bioavailability and PTX accumulation at tumor site. Later, histology studies confirmed nanoemulsion non-toxicity, expressing new opportunities and potential to treat breast cancer. These results suggested that current nanoformulation can be a potential therapeutic approach to effectively address breast cancer therapy.

Decoding the Postulated Entourage Effect of Medicinal Cannabis: What It Is and What It Isn't

The 'entourage effect' term was originally coined in a pre-clinical study observing endogenous bio-inactive metabolites potentiating the activity of a bioactive endocannabinoid. As a hypothetical afterthought, this was proposed to hold general relevance to the usage of products based on Cannabis sativa L. The term was later juxtaposed to polypharmacy pertaining to full-spectrum medicinal Cannabis products exerting an overall higher effect than the single compounds. Since the emergence of the term, a discussion of its pharmacological foundation and relevance has been ongoing. Advocates suggest that the 'entourage effect' is the reason many patients experience an overall better effect from full-spectrum products. Critics state that the term is unfounded and used primarily for marketing purposes in the Cannabis industry. This scoping review aims to segregate the primary research claiming as well as disputing the existence of the 'entourage effect' from a pharmacological perspective. The literature on this topic is in its infancy. Existing pre-clinical and clinical studies are in general based on simplistic methodologies and show contradictory findings, with the clinical data mostly relying on anecdotal and real-world evidence. We propose that the 'entourage effect' is explained by traditional pharmacological terms pertaining to other plant-based medicinal products and polypharmacy in general (e.g., synergistic interactions and bioenhancement).

Biomedical,clinical and environmental applications of platinum-based nanohybrids: An updated review

Platinum nanoparticles (Pt NPs) have numerous applications in various sectors, including pharmacology, nanomedicine, cancer therapy, radiotherapy, biotechnology and environment mitigation like removal of toxic metals from wastewater, photocatalytic degradation of toxic compounds, adsorption, and water splitting. The multifaceted applications of Pt NPs because of their ultra-fine structures, large surface area, tuned porosity, coordination-binding, and excellent physiochemical properties. The various types of nanohybrids (NHs) of Pt NPs can be fabricated by doping with different metal/metal oxide/polymer-based materials. There are several methods to synthesize platinum-based NHs, but biological processes are admirable because of green, economical, sustainable, and non-toxic. Due to the robust physicochemical and biological characteristics of platinum NPs, they are widely employed as nanocatalyst, antioxidant, antipathogenic, and anticancer agents. Indeed, Pt-based NHs are the subject of keen interest and substantial research area for biomedical and clinical applications. Hence, this review systematically studies antimicrobial, biological, and environmental applications of platinum and platinum-based NHs, predominantly for treating cancer and photo-thermal therapy. Applications of Pt NPs in nanomedicine and nano-diagnosis are also highlighted. Pt NPs-related nanotoxicity and the potential and opportunity for future nano-therapeutics based on Pt NPs are also discussed.

Integrated multiomic analysis and high-throughput screening reveal potential gene targets and synergetic drug combinations for osteosarcoma therapy

Although great advances have been made over the past decades, therapeutics for osteosarcoma are quite limited. We performed long-read RNA sequencing and tandem mass tag (TMT)-based quantitative proteome on osteosarcoma and the adjacent normal tissues, next-generation sequencing (NGS) on paired osteosarcoma samples before and after neoadjuvant chemotherapy (NACT), and high-throughput drug combination screen on osteosarcoma cell lines. Single-cell RNA sequencing data were analyzed to reveal the heterogeneity of potential therapeutic target genes. Additionally, we clarified the synergistic mechanisms of doxorubicin (DOX) and HDACs inhibitors for osteosarcoma treatment. Consequently, we identified 2535 osteosarcoma-specific genes and several alternative splicing (AS) events with osteosarcoma specificity and/or patient heterogeneity. Hundreds of potential therapeutic targets were identified among them, which showed the core regulatory roles in osteosarcoma. We also identified 215 inhibitory drugs and 236 synergistic drug combinations for osteosarcoma treatment. More interestingly, the multiomic analysis pointed out the pivotal role of HDAC1 and TOP2A in osteosarcoma. HDAC inhibitors synergized with DOX to suppress osteosarcoma both in vitro and in vivo. Mechanistically, HDAC inhibitors synergized with DOX by downregulating SP1 to transcriptionally modulate TOP2A expression. This study provided a comprehensive view of molecular features, therapeutic targets, and synergistic drug combinations for osteosarcoma.

Synthesis and Structure Determination of Substituted Thiazole Derivatives as EGFR/BRAFV600E Dual Inhibitors Endowed with Antiproliferative Activity

2,3,4-trisubstituted thiazoles 3a-i, having a methyl group in position four, were synthesized by the reaction of 1,4-disubstituted thiosemicarbazides with chloroacetone in ethyl acetate/Et₃N at room temperature or in ethanol under reflux. The structures of new compounds were determined using NMR spectroscopy, mass spectrometry, and elemental analyses. Moreover, the structure of compound 3a was unambiguously confirmed with X-ray analysis. The cell viability assay of 3a-i at 50 µM was greater than 87%, and none of the tested substances were cytotoxic. Compounds 3a-i demonstrated good antiproliferative activity, with GI₅₀ values ranging from 37 to 86 nM against the four tested human cancer cell lines, compared to the reference erlotinib, which had a GI₅₀ value of 33 nM. The most potent derivatives were found to be compounds 3a, 3c, 3d, and 3f, with GI₅₀ values ranging from 37 nM to 54 nM. The EGFR-TK and BRAF^V600E inhibitory assays' results matched the antiproliferative assay's results, with the most potent derivatives, as antiproliferative agents, also being the most potent EGFR and BRAF^V600E inhibitors. The docking computations were employed to investigate the docking modes and scores of compounds 3a, 3c, 3d, and 3f toward BRAF^V600E and EGFR. Docking computations demonstrated the good affinity of compound 3f against BRAF^V600E and EGFR, with values of -8.7 and -8.5 kcal/mol, respectively.