Search

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: 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]

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]

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]

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]

Copyright of Journal of Shenyang Pharmaceutical University is the property of Shenyang Pharmaceutical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The irritable bowel syndrome (IBS) is one of the most prevalent functional gastrointestinal disorders. However, at most, one-third of IBS patients are satisfied with the results of their treatment. A combination of pharmaceuticals or multi-target drugs should be used for the treatment to be effective due to the complexity of IBS pathophysiology. The Russian Expert Panel in gastroenterology, including functional gastrointestinal disorders, currently considered to be disorders of gut-brain interaction, at its open session has discussed the possibilities to increase the efficacy of treatment of patients with IBS with a multitargeted agent alverine citrate combined with simethicone registered under the trade name of Meteospasmyl® (Mayoly Pharma, France) and adopted the respective resolution. Based on the results of multiple experimental and clinical studies, the experts declared that the combination of alverine citrate with simethicone relieves pain through the alleviation of its main mechanisms (spasms, flatulence, visceral hypersensitivity, and inflammation), and normalizes bowel movements demonstrating the normokinetic (eukinetic) properties. All clinical effects of the combination of alverine citrate and simethicone are prolonged. Alverine citrate combined with simethicone improves patients’ quality of life, including their psychological well-being. Due to the efficacy and good safety profile, it is possible to use the combination of alverine citrate and simethicone as on-demand and ex juvantibus treatment. The combination of alverine citrate and simethicone is a single selective antispasmodic that allows for a simultaneous solution of two clinical challenges: to rid the patient of abdominal pains and to properly prepare the patient for instrumental examinations (colonoscopy, abdominal ultrasound, and radiological examination), due to a high simethicone dose and spasmolytic properties of alverine citrate. In addition, Meteospasmyl® is a single antispasmodic, which has demonstrated high efficacy and safety in a clinical study of its combination with a probiotic (Probiolog® IBS), containing the strains (Lactobacillus plantarum CECT 7484, Lactobacillus plantarum CECT 7485, and Pediococcus acidilactici CECT 7483) acting on all IBS pathogenesis steps. This is extremely important, considering the proven role of gut dysbiosis in IBS pathophysiology.

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 Hypertension is a major risk factor for cardiovascular disease. Antihypertensive drug therapy should not only consider the characteristics of the patient's blood pressure but also the patient's comorbid conditions. Currently, there is a lack of research on the medication status and influencing factors of hypertensive patients based on family doctor services. Objective To investigate the current medication status of hypertensive patients who purchased family doctor contract services in Jieshou City, Anhui Province, to describe the association between patient medication behavior and patient characteristics, to explore the influencing factors of medication adjustment, and to analyze the rationality of medication use in primary hypertensive patients. Methods Using cluster sampling, from July to August 2021, 48 administrative villages were randomly selected from Jieshou City, Anhui. Data on patient characteristics and medication were collected through face-to-face interviews using a self-made questionnaire. According to the "National Guidelines for the Prevention and Management of Hypertension at the Primary Level (2020 Edition) ", the antihypertensive drugs mentioned by patients in the questionnaire were divided into five categories: category A includes angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), category B includes beta-blockers, category C includes calcium channel blockers (CCBs), category D includes diuretics, and category E includes single-pill combination drugs. Blood pressure data uploaded by patients over the past year were obtained from the backend of iFLYTEK's intelligent voice blood pressure monitor to analyze the medication behavior of patients with different characteristics. Multivariate Logistic regression analysis was used to explore the influencing factors of medication adjustment in hypertensive patients. In this study, "combination medication" refers to taking a combination drug or two or more antihypertensive drugs, and "medication adjustment" refers to patients previously taking other antihypertensive drugs. Results A total of 3, 005 hypertensive patients were included in this study, including 1 291 males (43.0%) and 1 714 females (57.0%), with an average age of (65.5±9.8) years. The medication rate of hypertension was 79.1%, and the rate of combination medication was 40.2%. Among the 2 376 patients taking antihypertensive drugs, the rates of different types of antihypertensive drugs from high to low were (some patients had combination medication) : category E (39.6%), category C (35.1%), category D (20.3%), category A (20.1%), and category B (3.7%) ; the most frequently taken antihypertensive drug was compound lisinopril (33.7%). For patients with an average annual blood pressure ≥160/100 mmHg, 12.2% and 4.9% still did not take antihypertensive drugs. Patients' combination medication mainly involved category E antihypertensive drugs. For patients with an average annual "diastolic pressure≥100 mmHg" and "with complications", the rates of adjusted category A and C antihypertensive drugs increased relatively more; for patients with an average annual "systolic pressure ≥160 mmHg" and "without complications", the rate of adjusted category E antihypertensive drugs increased relatively more. Multivariate Logistic regression results showed that longer duration of medication (OR=1.042, 95%CI=1.031-1.053, P

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 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 While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. Scientific contribution This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.

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 Background Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level. Methods In this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules. Results To evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI. Conclusions AERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.

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]

While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. Scientific contribution: This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines. [ABSTRACT FROM AUTHOR]

Proteolysis‐targeting chimeras (PROTACs) have emerged as a promising strategy for targeted protein degradation and drug discovery. To overcome the inherent limitations of conventional PROTACs, an innovative drugtamer‐PROTAC conjugation approach is developed to enhance tumor targeting and antitumor potency. Specifically, a smart prodrug is designed by conjugating "drugtamer" to a nicotinamide phosphoribosyltransferase (NAMPT) PROTAC using a tumor microenvironment responsible linker. The "drugtamer" consists of fluorouridine nucleotide and DNA‐like oligomer. Compared to NAMPT PROTAC and the combination of PROTAC + fluorouracil, the designed prodrug AS‐2F‐NP demonstrates superior tumor targeting, efficient cellular uptake, improved in vivo potency and reduced side effects. This study provides a promising strategy for the precise delivery of PROTAC and synergistic antitumor agents. [ABSTRACT FROM AUTHOR]

In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I–II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose–response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well. [ABSTRACT FROM AUTHOR]

Background: Human epidermal growth factor receptor 2 (HER2)-positive gastric cancer (GC) is a heterogeneous GC subtype characterized by the overexpression of HER2. To date, few specific targeted therapies have demonstrated durable efficacy in HER2-positive GC patients, with resistance to trastuzumab typically emerging within 1 year. However, the mechanisms of resistance to trastuzumab remain incompletely understood, presenting a significant challenge to clinical practice. Methods: In this study, we integrated genetic screening and bulk transcriptome and epigenomic profiling to define the mechanisms mediating adaptive resistance to HER2 inhibitors and identify potential effective therapeutic strategies for treating HER2-positive GCs. Results: We revealed a potential association between adaptive resistance to trastuzumab in HER2-positive GC and the expression of YES-associated protein (YAP). Notably, our investigation revealed that long-term administration of trastuzumab triggers extensive chromatin remodeling and initiates YAP gene transcription in HER2-positive cells characterized by the initial inhibition and subsequent reactivation. Furthermore, treatment of HER2-positive GC cells and cell line-derived xenografts (CDX) models with YAP inhibitors in combination with trastuzumab was found to induce synergistic effects through the AKT/mTOR and ERK/mTOR pathways. Conclusion: These findings underscore the pivotal role of reactivated YAP and mTOR signaling pathways in the development of adaptive resistance to trastuzumab and may serve as a promising joint target to overcome resistance to trastuzumab. [ABSTRACT FROM AUTHOR]

The clinical management of malignant tumours is challenging, often leading to severe adverse effects and death. Drug resistance (DR) antagonises the effectiveness of treatments, and increasing drug dosage can worsen the therapeutic index (TI). Current efforts to overcome DR predominantly involve the use of drug combinations, including applying multiple anti-cancerous drugs, employing drug sensitisers, which are chemical agents that enhance pharmacokinetics (PK), including the targeting of cellular pathways and regulating pertinent membrane transporters. While combining multiple compounds may lead to drug–drug interactions (DDI) or polypharmacy effect, the use of drug sensitisers permits rapid attainment of effective treatment dosages at the disease site to prevent early DR and minimise side effects and will reduce the chance of DDI as lower drug doses are required. This review highlights the essential use of TI in evaluating drug dosage for cancer treatment and discusses the lack of a unified standard for TI within the field. Commonly used benefit–risk assessment criteria are summarised, and the critical exploration of the current use of TI in the pharmaceutical industrial sector is included. Specifically, this review leads to the discussion of drug sensitisers to facilitate improved ratios of effective dose to toxic dose directly in humans. The combination of drug and sensitiser molecules might see additional benefits to rekindle those drugs that failed late-stage clinical trials by the removal of detrimental off-target activities through the use of lower drug doses. Drug combinations and employing drug sensitisers are potential means to combat DR. The evolution of drug combinations and polypharmacy on TI are reviewed. Notably, the novel binary weapon approach is introduced as a new opportunity to improve TI. This review emphasises the urgent need for a criterion to systematically evaluate drug safety and efficiency for practical implementation in the field. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: Synergistic medication, a crucial therapeutic strategy in cancer treatment, involves combining multiple drugs to enhance therapeutic effectiveness and mitigate side effects. Current research predominantly employs deep learning models for extracting features from cell line and cancer drug structure data. However, these methods often overlook the intricate nonlinear relationships within the data, neglecting the distribution characteristics and weighted probability densities of gene expression data in multi-dimensional space. It also fails to fully exploit the structural information of cancer drugs and the potential interactions between drug molecules. Methods: To overcome these challenges, we introduce an innovative end-toend learning model specifically tailored for cancer drugs, named Dual Kernel Density and Positional Encoding (DKPE) for Graph Synergy Representation Network (DKPEGraphSYN). This model is engineered to refine the prediction of drug combination synergy effects in cancer. DKPE-GraphSYN utilizes Dual Kernel Density Estimation and Positional Encoding techniques to effectively capture the weighted probability density and spatial distribution information of gene expression, while exploring the interactions and potential relationships between cancer drug molecules via a graph neural network. Results: Experimental results show that our prediction model achieves significant performance enhancements in forecasting drug synergy effects on a comprehensive cancer drug and cell line synergy dataset, achieving an AUPR of 0.969 and an AUC of 0.976. Discussion: These results confirm our model's superior accuracy in predicting cancer drug combinations, providing a supportive method for clinical medication strategy in cancer. [ABSTRACT FROM AUTHOR]

AbstractDrug combinations are increasingly used in cancer treatment to enhance drug effectiveness through synergistic therapeutic effects. However, determining the optimal biological dose combination (OBDC) in small-scale drug combination trials presents challenges due to the increased complexity of the dose space. To effectively optimize the dose combination of combined drugs, we propose a model-assisted design by extending the single-agent Bayesian optimal interval phase I/II (BOIN12) design. Our approach incorporates a utility function to balance the tradeoff between risk and benefit and directly models the utility of each dose by constructing a quasi-beta-binomial model. A key advantage of our design is the simplification of decision-making during interim periods by considering all possible outcomes and pre-including the decision rule in the protocol. Additionally, we present a time-to-event (TITE) version of our design, employing an approximate likelihood approach to mitigate potential late-onset effects. We demonstrate that our proposed design exhibits robust and desirable operating characteristics across various scenarios through extensive simulation studies. [ABSTRACT FROM AUTHOR]

Background: Microorganisms in biofilms are particularly difficult to control because of their increased survival and antibiotic resistance. Allicin and domiphen were employed to inhibit the microbial growth and biofilm formation of Staphylococcus aureus, Escherichia coli, and Candida albicans strains. Methods: Broth microdilution method and checkerboard assay were conducted to determine the efficacy of allicin combined with domiphen against S. aureus, E. coli, and C. albicans. Microbial biofilm formation was measured using the crystal violet staining method and fluorescence microscopy. And the total viable count of the biofilm cells on material surface after the treatment with antimicrobial reagents was calculated with the plate count technique. Results: The two drugs showed synergistic effects against the pathogens with a fractional bactericidal concentration of less than 0.38. The combination of 64 µg/mL allicin with 1 µg/mL domiphen dispersed over 50% of the biofilm mass of S. aureus, E. coli, and C. albicans. In addition, the drug combination reduced the total viable counts of E. coli and C. albicans biofilm cells on stainless steel and polyethylene surfaces by more than 102 CFU/mL. Conclusion: The combination of allicin and domiphen is an effective strategy for efficiently decreasing biofilms formation on various industrial materials surfaces. [ABSTRACT FROM AUTHOR]

Combination therapy has emerged as an efficacy strategy for treating complex diseases. Its potential to overcome drug resistance and minimize toxicity makes it highly desirable. However, the vast number of potential drug pairs presents a significant challenge, rendering exhaustive clinical testing impractical. In recent years, deep learning‐based methods have emerged as promising tools for predicting synergistic drug combinations. This review aims to provide a comprehensive overview of applying diverse deep‐learning architectures for drug combination prediction. This review commences by elucidating the quantitative measures employed to assess drug combination synergy. Subsequently, we delve into the various deep‐learning methods currently employed for drug combination prediction. Finally, the review concludes by outlining the key challenges facing deep learning approaches and proposes potential challenges for future research. [ABSTRACT FROM AUTHOR]

Combination therapy is increasingly being explored as a promising approach for improving cancer treatment outcomes. However, identifying effective dose combinations in early oncology drug development is challenging due to limited sample sizes in early-phase clinical trials. This task becomes even more complex when multiple agents are being escalated simultaneously, potentially leading to a loss of monotonic toxicity order with respect to the dose. Traditional single-agent trial designs are insufficient for this multi-dimensional problem, necessitating the development and implementation of dose-finding methods specifically designed for drug combinations. While, in practice, approaches to this problem have focused on preselecting combinations with a known toxicity order and applying single-agent designs, this limits the number of combinations considered and may miss promising dose combinations. In recent years, several novel designs have been proposed for exploring partially ordered drug combination spaces with the goal of identifying a maximum tolerated dose combination, based on safety, or an optimal dose combination, based on toxicity and efficacy. However, their implementation in clinical practice remains limited. In this article, we describe the application of the partial order continual reassessment method and its extensions for combination therapies in early-phase clinical trials. We present completed trials that use safety endpoints to identify maximum tolerated dose combinations and adaptively use both safety and efficacy endpoints to determine optimal treatment strategies. We discuss the effectiveness of the partial-order continual reassessment method and its extensions in identifying optimal treatment strategies and provide our experience with executing these novel adaptive designs in practice. By utilizing innovative dose-finding methods, researchers and clinicians can more effectively navigate the challenges of combination therapy development, ultimately improving patient outcomes in the treatment of cancer. [ABSTRACT FROM AUTHOR]

Purpose: Artemisinin combination therapies, the first-line antimalarials in Nigeria, have reportedly suffered multiple failures in malaria treatment, hence the search for novel combination of other compounds. Methyl gallate and palmatine have been reported to exhibit antiplasmodial activities but the antimalarial activity of their combination has not been evaluated. Therefore, the evaluation of the combination of methyl gallate and palmatine for antimalarial activity in vitro and in vivo in the presence of piperine was carried out. Materials and Methods: The inhibitory potential of methyl gallate and palmatine combination on β-hematin (hemozoin) formation was studied in vitro. Also, the antimalarial activity of methyl gallate and palmatine combination with/without a bioenhancer (piperine) was evaluated in Plasmodium berghei NK65-infected mice. Results: Methyl gallate and palmatine in the ratio 3:2 acted synergistically in vitro and had the highest inhibitory effect (IC50 = 0.73 µg/mL) on β-hematin (hemozoin) formation. The 3:2 combination of methyl gallate and palmatine exhibited no antimalarial activity in vivo in the absence of piperine but caused reduction in parasitemia that exceeded 40% in the presence of piperine at the dose of 25 mg/kg body weight on days 6 and 8 post-inoculation in mice. Conclusion: The 3:2 combination of methyl gallate and palmatine in the presence of piperine exhibited antimalarial activity in vivo, possibly by synergistic inhibition of hemozoin formation which may cause accumulation of haem within the food vacuole of Plasmodium spp. and its death. [ABSTRACT FROM AUTHOR]

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that regulates genes of drug transporters and metabolic enzymes to detoxify small molecule xenobiotics. It has a complex role in cancer biology, influencing both the progression and suppression of tumors by modulating malignant properties of tumor cells and anti-tumor immunity, depending on the specific tumor type and developmental stage. This has led to the discovery and development of selective AhR modulators, including BAY 2416964 which is currently in clinical trials. To identify small molecule anticancer agents that might be combined with AhR antagonists for cancer therapy, a high-throughput combination screen was performed using multi-cell type tumor spheroids grown from malignant cells, endothelial cells, and mesenchymal stem cells. The AhR selective antagonists BAY 2416964, GNF351, and CH-223191 were tested individually and in combination with twenty-five small molecule anticancer agents. As single agents, BAY 2416964 and CH-223191 showed minimal activity, whereas GNF351 reduced the viability of some spheroid models at concentrations greater than 1 µM. The activity of most combinations aligned well with the single agent activity of the combined agent, without apparent contributions from the AhR antagonist. All three AhR antagonists sensitized tumor spheroids to TAK-243, an E1 ubiquitin-activating enzyme inhibitor. These combinations were active in spheroids containing bladder, breast, ovary, kidney, pancreas, colon, and lung tumor cell lines. The AhR antagonists also potentiated pevonedistat, a selective inhibitor of the NEDD8-activating enzyme E1 regulatory subunit, in several tumor spheroid models. In contrast, the AhR antagonists did not enhance the cytotoxicity of the proteasome inhibitor bortezomib.

Abstract 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.

Background and purpose: Trichosanthin (TCS) is a plant-based ribosome-inactivating protein exhibiting a range of pharmacological properties, including abortifacient and anticancer. However, the routine clinical use in cancer treatment was hampered by its antigenicity. Hexokinase 2 (HK2) is a pivotal regulator of glycolysis, where aberrant expression is observed in many cancers. This study investigates the anticancer effects and mechanisms of TCS in combination with benserazide (Benz), a HK2 inhibitor, in Hela and SCC25 cancer models. Experimental approach: MTT, colony-formation and cell cycle assays were performed to assess the cytotoxic effects of TCS and Benz in HeLa and SCC25 cells. Seahorse assay, western blotting, flow cytometry analysis and RNA sequencing were employed to investigate the pharmacological effects of the combo treatment. SCC25 cell xenograft mouse model was established for in vivo efficacy study. Key results: Combined use of TCS and Benz exhibited synergistic anticancer effects in both Hela and SCC25 cell models. The observed synergistic effects were attributed to the modulation of glycolysis by targeting HK2, leading to reduced lactate production and increased ROS accumulation which further inhibited colony formation and cell cycle progression, as well as triggered apoptosis. Moreover, this combination effectively inhibited NFκB/ERK signalling pathways, which were found to be significantly activated upon single use of TCS. It was found that the combination significantly suppressed the tumour growth in SCC25 cell xenograft mouse model. Conclusion: Our findings suggested that targeting HK2 and modulating glycolysis may offer a promising avenue for improving the therapeutic outcomes of TCS-based anticancer treatments.

Fungal infections, though affecting healthcare globally, receive insufficient attention in clinical and academic settings. Invasive fungal infections, particularly caused by combat wounds, have been identified as a critical threat by the US Department of Defense. Monotherapy with traditional antifungals is often insufficient, and so combination therapies are explored to enhance treatment efficacy. However, systemic combination treatments can result in severe adverse effects, suggesting the need for localised delivery systems, such as drug-loaded electrospun patches, to administer antifungals directly to the infection site. This proof-of-concept study hypothesised that dual amorolfine and terbinafine therapy slowly releasing from electrospun patches would be an effective way of eradicating Candida albicans when the patch was applied directly to the fungal colony. The feasibility of creating electrospun materials loaded with amorolfine and terbinafine for combination antifungal therapy was investigated. Electrospinning was used to fabricate polycaprolactone (PCL) patches with varying drug loadings (2.5%, 5%, and 10% w/w) of amorolfine and terbinafine either individually or in combination. The incorporation of both drugs in the fibres was confirmed, with the drugs predominantly in an amorphous state. Results showed that combination therapy patches had a significantly greater and prolonged antifungal effect compared to monotherapy patches, with larger zones of inhibition and sustained efficacy over at least 7 days. This study therefore demonstrates that PCL-based electrospun patches containing amorolfine and terbinafine provide superior antifungal activity against C. albicans compared to monotherapy patches. This approach could lower required drug doses, reducing adverse effects, and enhance patient compliance due to prolonged drug release, leading to more effective antifungal therapy.

Abstract Motivation Drug combination therapies have shown promise in clinical cancer treatments. However, it is hard to experimentally identify all drug combinations for synergistic interaction even with high-throughput screening due to the vast space of potential combinations. Although a number of computational methods for drug synergy prediction have proven successful in narrowing down this space, fusing drug pairs and cell line features effectively still lacks study, hindering current algorithms from understanding the complex interaction between drugs and cell lines. Results In this paper, we proposed a Permutable feature fusion network for Drug-Drug Synergy prediction, named PermuteDDS. PermuteDDS takes multiple representations of drugs and cell lines as input and employs a permutable fusion mechanism to combine drug and cell line features. In experiments, PermuteDDS exhibits state-of-the-art performance on two benchmark data sets. Additionally, the results on independent test set grouped by different tissues reveal that PermuteDDS has good generalization performance. We believed that PermuteDDS is an effective and valuable tool for identifying synergistic drug combinations. It is publicly available at https://github.com/littlewei-lazy/PermuteDDS . Scientific contribution First, this paper proposes a permutable feature fusion network for predicting drug synergy termed PermuteDDS, which extract diverse information from multiple drug representations and cell line representations. Second, the permutable fusion mechanism combine the drug and cell line features by integrating information of different channels, enabling the utilization of complex relationships between drugs and cell lines. Third, comparative and ablation experiments provide evidence of the efficacy of PermuteDDS in predicting drug-drug synergy.

Abstract Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is particularly important given the increase in the number of available drug classes and potential drug-drug interactions. Existing methods for predicting the synergistic effects of drug combinations primarily focus on extracting structural features of drug molecules and cell lines, but neglect the interaction mechanisms between cell lines and drug combinations. Consequently, there is a deficiency in comprehensive understanding of the synergistic effects of drug combinations. To address this issue, we propose a drug combination synergy prediction model based on multi-source feature interaction learning, named MFSynDCP, aiming to predict the synergistic effects of anti-tumor drug combinations. This model includes a graph aggregation module with an adaptive attention mechanism for learning drug interactions and a multi-source feature interaction learning controller for managing information transfer between different data sources, accommodating both drug and cell line features. Comparative studies with benchmark datasets demonstrate MFSynDCP's superiority over existing methods. Additionally, its adaptive attention mechanism graph aggregation module identifies drug chemical substructures crucial to the synergy mechanism. Overall, MFSynDCP is a robust tool for predicting synergistic drug combinations. The source code is available from GitHub at https://github.com/kkioplkg/MFSynDCP .