Combination Of Anticancer Drugs Research Articles

Development of Graphene Oxide-Based Anticancer Drug Combination Functionalized with Folic Acid as Nanocarrier for Targeted Delivery of Methotrexate.

Graphene has become a prominent material in cancer research in recent years. Graphene and its derivatives also attract attention as carriers in drug delivery systems. In this study, we designed a graphene oxide (GO)-based methotrexate (MTX)-loaded and folic acid (FA)-linked drug delivery system. MTX and FA were bound to GO synthesized from graphite. MTX/FA/GO drug delivery system and system components were characterized using Fourier transform infrared spectroscopy (FTIR), differential calorimetric analysis (DSC), scanning electron microscopy (SEM), transmission electron microscopy (TEM), zeta potential analysis, and dimension measurement (DLS) studies. SEM and TEM images confirmed the nanosheet structure of GO synthesized from graphite, and it was shown that MTX/FA binding to GO transformed the two-dimensional GO into a three-dimensional structure. FTIR and DSC graphs confirmed that oxygen atoms were bound to GO with the formation of carboxylic, hydroxyl, epoxide, and carbonyl groups as a result of the oxidation of graphite, and GO was successfully synthesized. Additionally, these analyses showed that MTX and FA bind physicochemically to the structure of GO. The in vitro Franz diffusion test was performed as a release kinetic test. The release kinetics mathematical model and correlation coefficient (R2) of MTX-loaded GO/FA nanomaterials were found to be the Higuchi model and 0.9785, respectively. Stiffness analyses showed that adding FA to this release system facilitated the entry of the drug into the cell by directing the system to target cells. As a result of the stiffness analyses, the stiffness values of the control cell group, free MTX, and MTX/FA/GO applied cells were measured as 2.34 kPa, 1.87 kPa, and 1.56 kPa, respectively. According to these results, it was seen that MTX/FA/GO weakened the cancer cells. Combined use of the MTX/FA/GO drug delivery system had a higher cytotoxic effect than free MTX on the MDA-MB-231 breast cancer cell line. The results showed that the synthesized MTX/FA/GO material has promising potential in cancer cell-specific targeted therapy for MTX as a drug delivery system.

Cardiotoxicity in Cancer Patients: The Prevalence, Risk Factors, and Cardioprotective Measures in a Cancer Centre in Saudi Arabia.

Chemotherapy-related cardiotoxicity can exhibit several patterns of functional, structural, and vascular complications. This study aims to identify the patterns and the factors associated with cardiotoxicity in cancer patients. A retrospective cross-sectional analysis of 96 adult cancer patients undergoing anticancer therapy was investigated at King Khalid Hospital in Najran, Saudi Arabia, from May 2022 to April 2023. The data on patient and cancer characteristics, treatment, and outcomes were collected and analyzed. Factors associated with cardiotoxicity were investigated through univariate analyses using odds ratio (OR) and 95% confidence interval (CI). Among the 96 cancer patients in the study, cardiotoxicity occurred in 12 individuals (12.5%). The mean age was 57.0 ± 13.3 years (range: 32-81 years), with 32 (33.3%) being above 65 years. The most common comorbidities were diabetes (n=48; 50%), followed by hypertension (n=32; 33.3%), and dyslipidemia (n=20; 20.8%). The most common cancers were gastrointestinal cancer (n=32; 33.3%), followed by breast cancer (n=22; 22.9%) and lymphoma (n=14; 14.6%). Females were disproportionately affected (64.6%), with 57.3% of them in the metastatic stage. The majority of patients (90.6%) had normal ejection fraction before chemotherapy initiation. In univariate analysis, current smoking (OR: 7.00; 95%CI: 1.94-25.25, p= 0.003), history of percutaneous cardiac intervention (OR: 40.24; 95%CI: 1.80-896.26, p= 0.019), diabetes (OR: 6.05; 95%CI: 1.24-29.32, p= 0.025), renal failure (OR: 8.20; 95%CI: 0.91-74.88, p= 0.046), dyslipidemia (OR: 5.00; 95 CI: 1.38-18.32, p=0.012), anthracycline use (OR: 18.33; 95%CI: 4.36-126.55, p <0.001), trastuzumab use (OR: 25.00; 95%CI: 6.25-129.86, p < 0.001), and increased chemotherapy cycles number (> 10 cycles) (OR: 73.00; 95%CI: 8.56- 622.36, p < 0.001) were associated with cardiotoxicity. Additionally, beta-blocker use was associated with lower rates of cardiotoxicity (OR: 0.17; 95%CI: 0.036-0.84, p= 0.029). The incidence of cardiotoxicity among cancer patients treated with chemotherapy is modest, difficult to predict, and independent of baseline cardiac systolic functions. Factors associated with cardiotoxicity include smoking, history of percutaneous cardiac intervention, diabetes, renal failure, dyslipidemia, anthracycline or trastuzumab use, and increased chemotherapy cycle numbers. A combination of various anticancer drugs and chemotherapy may dramatically raise the risk of cardiotoxicity in cancer patients. As a result, patients receiving high-risk cardiotoxic drugs should be monitored with caution to avoid drug-related cardiotoxicity. Furthermore, proactive treatment techniques aiming at reducing the possible cardiotoxic effects of anticancer therapy are critical.

Cancer associated fibroblasts modulate the cytotoxicity of anti-cancer drugs in breast cancer: An in vitro study.

Tumour microenvironment (TME) contributes to resistance to anti-cancer drugs through multiple mechanisms including secretion of pro-survival factors by cancer associated fibroblasts (CAFs). In this study, we determined the chemotherapy resistance producing potential of CAFs in molecular subtypes of breast cancer. The CAFs were isolated from fresh lumpectomy/mastectomy specimens of different molecular subtypes of breast cancer. The CAFs were cultured and secretome was collected from each breast cancer subtype. Breast cancer cell lines MCF-7, SK-BR3, MDA-MB-231, and MDA-MB-468 were treated with different doses of tamoxifen, trastuzumab, cisplatin, and doxorubicin alone respectively and in combination with secretome of CAFs from respective subtypes. MTT assay was done to check cell death after drug treatment. Liquid chromatography-mass spectrometry (LCMS) analysis of CAF secretome was also done. MTT assay showed that anti-cancer drugs alone had growth inhibitory effect on the cancer cells however, presence of CAF secretome reduced the anti-cancer effect of the drugs. Resistant to drugs in the presence of secretome, was determined by increased cell viability i.e., MCF-7, 51.02% to 63.02%; SK-BR-3, 34.22% to 44.88%; MDA-MB-231, 52.59% to 78.63%; and MDA-MB-468, 48.92% to 55.08%. LCMS analysis of the secretome showed the differential abundance of CAFs secreted proteins across breast cancer subtypes. The treatment of breast cancer cell lines with anti-cancer drugs in combination with secretome isolated from molecular subtype specific CAFs, reduced the cytotoxic effect of the drugs. In addition, LCMS data also highlighted different composition of secreted proteins from different breast cancer associated fibroblasts. Thus, TME has heterogenous population of CAFs across the breast cancer subtypes and in vitro experiments highlight their contribution to chemotherapy resistance which needs further validation.

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.

Spheroid Drug Sensitivity Screening in Glioma Stem Cell Lines.

In vitro drug sensitivity screens are important tools in the discovery of anti-cancer drug combination therapies. Typically, these in vitro drug screens are performed on cells grown in a monolayer. However, these two-dimensional (2D) models are considered less accurate compared to three-dimensional (3D) spheroid cell models; this is especially true for glioma stem cell lines. Cells grown in spheres activate different signaling pathways and are considered more representative of in vivo models than monolayer cell lines. This protocol describes a method for in vitro drug screening of spheroid lines; mouse and human glioma stem cell lines are used as an example. This protocol describes a 3D spheroid drug sensitivity and synergy assay that can be used to determine if a drug or drug combination induces cell death and if two drugs synergize. Glioma stem cell lines aremodified to express RFP. Cells are plated in low attachment round well bottom 96 plates, and spheres are allowed to form overnight. Drugs are added, and the growth is monitored by measuring the RFP signal over time using the Incucyte live imaging system,a fluorescence microscope embedded in the tissue culture incubator. Half maximal inhibitory concentration (IC50), median lethal dose (LD50), and synergy score are subsequently calculated to evaluate sensitivities to drugs alone or in combination. The three-dimensional nature of this assayprovides a more accurate reflection of tumor growth, behavior, and drug sensitivities in vivo, thus forming the basis for further preclinical investigation.

Comparison of Tepotinib, Paclitaxel, or Ramucirumab Efficacy According to the Copy Number or Phosphorylation Status of the MET Gene: Doublet Treatment versus Single Agent Treatment.

Although conventional combination chemotherapies for advanced gastric cancer (GC) increase survival, such therapies are associated with major adverse effects; more effective and less toxic treatments are required. Combinations of different anti-cancer drugs, for example, paclitaxel plus ramucirumab, have recently been used as second-line treatments for advanced GC. This study evaluated how copy number variations of the MET gene, MET mutations, and MET gene and protein expression levels in human GC cells modulate the susceptibility of such cells to single-agent (tepotinib, ramucirumab, or paclitaxel) and doublet (tepotinib-plus-paclitaxel or ramucirumab-plus-paclitaxel treatment regimens. Compared with ramucirumab-plus-paclitaxel, tepotinib-plus-paclitaxel better inhibited the growth of GC cells with MET exon 14 skipping mutations and those lacking MET amplification but containing phosphorylated MET; such inhibition was dose-dependent and associated with cell death. Tepotinib-plus-paclitaxel and ramucirumab-plus-paclitaxel similarly inhibited the growth of GC cells lacking MET amplification or MET phosphorylation, again in a dose-dependent manner, but without induction of cell death. However, tepotinib alone or tepotinib-plus-ramucirumab was more effective against c-MET-positive GC cells (>30 copy number variations) than was ramucirumab or paclitaxel alone or ramucirumab-plus-paclitaxel. These in vitro findings suggest that compared with ramucirumab-plus-paclitaxel, tepotinib-plus-paclitaxel better inhibits the growth of c-MET-positive GC cells, cells lacking MET amplification but containing phosphorylated MET, and cells containing MET mutations. Clinical studies are required to confirm the therapeutic effects of these regimens.

Natural product based anticancer drug combination discovery assisted by deep learning and network analysis

Drug combination therapies have shown effective performance in treating cancer through increased efficacy and circumvention of drug resistance through synergy. Two avenues can be used to discover drug combinations: a novel approach that utilizes natural products compared with the textbook approach of utilizing existing chemotherapy drug combinations. Many natural products achieve efficacy due to synergistic interactions between the active ingredients. Therefore, the pharmacophore relationships in herbal compounds which synergize can potentially be applied to chemotherapy drugs to drive combination discovery. Machine learning approaches have been developed to identify drug combinations, especially deep neural networks (DNN), which have achieved state-of-the-art performance in many drug discovery tasks. Here, a drug protein interaction (DPI) prediction DNN, DeepDPI, was developed to employ DPI drug representations and achieved state-of-the-art performance using the DrugBank database. Two DNNs were also developed to predict novel drug combinations: DeepNPD, which predicts combinations in natural products, and DeepCombo, which predicts synergy in chemotherapy drugs, using the HERB and DrugCombDB databases respectively. An ensemble architecture enhanced with a novel similarity based weight adjustment (SBWA) approach was used and both models accurately predicted drug combinations for both known and unknown drugs. Lastly, a screening was conducted using each model where DeepNPD predicted combinations where drugs had similar targets, while DeepCombo predicted combinations where one agent potentiated the other, with both models’ predicted combinations investigated through a network-based analysis and identifying as a synergistic combinations in literature. DeepNPD notably identified Thioguanine and Hydroxyurea and DeepCombo discovered Vinblastine and Dasatinib as hits for potential new anticancer drug combinations. DeepNPD illustrates how natural products are a novel path where new drug combinations can be discovered.

Glutathione depletion based Pt(IV) hybrid mesoporous organosilica delivery system to conquer cisplatin chemoresistance: A “one stone three birds” strategy

The occurrence of acquired resistance to cisplatin (DDP) that induces the toxic drug effects has always been a huge challenge and urgently needs to be resolved in the cancer treatment. The combination of anticancer drugs with different mechanisms can remarkably improve the chemotherapeutic efficiency. Given that glutathione (GSH) plays as the driving factors in the resistance of DDP, here we have firstly proposed a “three birds, one stone” based nanoplatform to achieve triple synergetic effects simultaneously addressing DDP resistance in non-small cell lung cancer (NSCLC). Specifically, we initially designed and synthesized a DDP prodrug [Pt(IV)] bridged silsesquioxane precursor (Pt-Si). Then Pt-Si and bis[3-(triethoxysilyl)propyl]diselenide (BTESePD) were integrated into the framework of mesoporous organosilica nanoparticles (MONs) to obtain a nanocarrier MONPt/Se. After loading with norcantharidin (NCTD) and modifying with the aptamer AS1411 based G-quadruplex (Apt), the Apt@NCTD@MONPt/Se exhibit impressive tumor homing capability. Once being endocytosed, (I) the diselenide and -O-Pt(IV)-O- rich scaffold can be reduced by the excessive GSH, followed by (II) breaking the redox homeostasis via GSH depletion and precise release of the DDP. Next, the encapsulated NCTD is also released along with the degradation of the nanocarriers thereby (III) achieving the GSH depletion and synergistic anti-tumor effect of NCTD and DDP. Taken together, we believe this “one stone, three birds” strategy may be a promising paradigm to conquer drug resistance for clinical care.

Diagnosis, combined treatment, and long-term follow-up of a thymoma patient

Thymic neoplasms account for 20 – 25% of primary mediastinal tumors. Nonspecific clinical symptoms are often associated with delayed diagnosis of thymoma, leading to a distinctive problem of late diagnosis. Nowadays, surgical removal of the tumor is the method of choice for thymoma treatment. Combination treatment is indicated in the case of an unresectable tumor. Modern therapy (various combinations of anticancer drugs) helps reduce neoplasm size, stop metastasis, and maintain good quality of life in patients with thymoma.Aim. The presented clinical case of type B3 thymoma (with invasion into surrounding organs and metastatic pleura) is characterized by a long asymptomatic period of the disease, the onset of symptoms after suffering COVID-19 (COronaVIrus Disease 2019) and successful long-term polychemotherapy. The causes of late diagnosis, as well as insufficient oncologic vigilance in evaluating the results of laboratory and instrumental tests, are discussed based on this clinical case.Conclusion. A correct diagnosis, as well as successful treatment, requires a multidisciplinary approach involving physicians from different specialties – general practitioners, oncologists, surgeons, radiologists, and morphologists.

Predicting anticancer synergistic drug combinations based on multi-task learning

BackgroundThe discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks.MethodsIn this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features.ResultsCompared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision–recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies.ConclusionOur study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.

A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs.

Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.

Quantifying anticancer drug toxicity on white blood cell count in cancer patients: A mathematical and computational approach

ObjectivesWhite blood cells (WBCs) are immune cells that fight infections and cancers. However, many cancers and their treatment with chemotherapy negatively affect the WBCs count. For oncologists and physicians, it is important to know how much the WBCs count is affected to optimize drug administration. The data of breast cancer patients were included in this study because the largest number of cancer patients were suffering from breast cancer in the under-study hospital. This study presents a computational and mathematical approach to measure the effect on the WBCs count of breast cancer patients treated with chemotherapy with two anticancer drug combinations: docetaxel with cyclophosphamide (TC) and doxorubicin with cyclophosphamide (AC). MethodsBetween July 2016 and October 2021, to breast cancer patients, 4 cycles of chemotherapy (one cycle of 21 days) were administered intravenously using TC (75 mg/m2 + 600 mg/m2). Similarly, varying in cases, intravenous AC (60 mg/m2 + 600 mg/m2) was administered in each of the 4 cycles. The WBCs counts of the subjects affected by both TC and AC combinations were observed for comparison. An equation was derived to calculate the effect of TC and AC on WBCs count (eWBCc). The eWBCc of 171 patients who received TC and 154 patients who received AC were calculated by implantation of the derived equation using Python 3.8. ResultsComparing the results of 171 patients, it is observed that, TC affected WBC count of a patient at lowest level by 12.29%, at maximum level by 69.64%, and on average by 34.13%. Whereas, comparing the results of 154 patients, AC affected WBC count of a patient at lowest level by 19%, at maximum level by 65.03%, and on average, by 44.36%. A paired t-test was used to analyze the statistical differences between the eWBCc of both TC and AC cohorts. This test indicates a statistically significant difference between the eWBCc by TC and AC, as evidenced by the P-value of 0.000, which is less than the chosen significance level (alpha) of 0.05. ConclusionWe conclude that TC remains less toxic than AC in affecting white blood cells count of the studied patients.

Antidepressants as Autophagy Modulators for Cancer Therapy.

Cancer is a major global public health problem with high morbidity. Depression is known to be a high-frequency complication of cancer diseases that decreases patients' life quality and increases the mortality rate. Therefore, antidepressants are often used as a complementary treatment during cancer therapy. During recent decades, various studies have shown that the combination of antidepressants and anticancer drugs increases treatment efficiency. In recent years, further emerging evidence has suggested that the modulation of autophagy serves as one of the primary anticancer mechanisms for antidepressants to suppress tumor growth. In this review, we introduce the anticancer potential of antidepressants, including tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), selective serotonin reuptake inhibitors (SSRIs), and serotonin-norepinephrine reuptake inhibitors (SNRIs). In particular, we focus on their autophagy-modulating mechanisms for regulating autophagosome formation and lysosomal degradation. We also discuss the prospect of repurposing antidepressants as anticancer agents. It is promising to repurpose antidepressants for cancer therapy in the future.

ROS Modulate Cell Death Mechanism in Cervical Cancer Cells Treated with the Combination of Polyphenolic Compounds and Anticancer Drug Cisplatin: A Review

Background: Most cervical cancer fatalities have been reported due to drug resistance, invasion, and metastasis. Combination therapy is a prominent technique for overcoming the toxicity of cancer chemotherapy to normal cells, which is mediated across numerous targeted pathways and requires a lower dose of each individual agent. Polyphenolic substances have the potential to improve chemotherapy efficacy while also reducing negative effects. Aims: This study aimed to review the research findings on the role of reactive oxygen species (ROS) in cervical cancer cell HeLa treated with combination therapy. Results: Hydroxyl radicals damage DNA, causing a cascade of structural changes in purine and pyrimidine bases that could lead to mutagenicity. ROS, such as hydroxyl radical (OH-), superoxide anions (O2 -), hydrogen peroxide (H2O2), and peroxyl radicals (ROO-), are frequent products of aerobic metabolism that can be beneficial or detrimental to the biological system. To combat the harmful effects of ROS, cells have an antioxidative defense system that comprises superoxide dismutases, catalase, glutathione, and other defensive mechanisms. Excessive ROS accumulation causes DNA damage, which triggers the apoptotic machinery, resulting in cell death. Conclusion: Chemotherapeutic medications with phenolic compounds or polyphenol-rich extracts exhibit anticancer synergy. Combination treatment with polyphenols and anticancer drugs is one of the promising approaches in the treatment of cervical cancer.

Autophagy induction using Resveratrol enhances the anti-cancer efficacy of Doxazosin in breast cancer cells.

The combination of anti-cancer drugs improves effectiveness compared to the mono-therapy scenario by targeting key pathways synergistically or in an additive way. Doxazosin (DOX) and Resveratrol (RES) are reported to have an anti-cancer impact against different cancer cells. Aim: To evaluate the anti-cancer properties of Doxazosin and Resveratrol, each alone or in combination, in inhibiting breast cancer cell proliferation. Methods: MCF-7 cancer cells were seeded to a confluent monolayer and treated with 100, 50, 25, 12.5, 6.25, and 3.12 µM of each drug alone and as a combination. Cytotoxicity was evaluated using dimethyl thiazolyl diphenyl tetrazolium salt (MTT) assay and colony formation assay. The immunocytochemistry technique was conducted to evaluate caspase-3 and GABARAP expression. Results: All the drugs have a significant effect on cancer cell proliferation. The combination treatment of Doxazosin-Resveratrol has a more cytotoxic effect than each drug alone, as well as in colony formation of MCF-7 cancer cells. In combination treatment, caspase-3 had a higher expression pattern than other treatments. Resveratrol treatment elevated GABARAP expression in cancer cells, indicating the induction of the autophagy process. Conclusion: Although each drug has its characteristic result of a cytotoxic effect, the combination of Doxazosin and Resveratrol synergizes the inhibition of MCF-7 cancer cell proliferation. Keywords: Resveratrol; Doxazosin; combination; MCF-7; Autophagy.

Molecular Investigation of the Antitumor Effects of Monoamine Oxidase Inhibitors in Breast Cancer Cells.

The catalytic activity of monoamine oxidase A (MAO-A) has been linked to tumorigenesis due to the production of reactive oxygen species (ROS) and the resulting oxidative stress. MAO-A inhibition revealed a beneficial role in prostate and lung cancer treatment. This study is aimed at evaluating the effect of different monoamine oxidase A inhibitors (MAO-AIs) on the proliferation and progression of breast cancer cell lines. The cell viability assay was used to evaluate the antiproliferative and combined effects of MAO-AIs. Cell migration was evaluated using wound healing, invasion, and colony formation assays. The underlying mechanism of cell death was studied using flow cytometry. The real-time polymerase chain reaction was used to determine the relative gene expression. Finally, MAO-A activity in breast cancer cells was evaluated using an MAO-A activity assay. According to the results, the examined MAO-AIs significantly inhibited the proliferation of breast cancer cells in a dose-dependent manner. In breast cancer cells, the combination of anticancer drugs (doxorubicin or raloxifene) with MAO-AIs resulted in a synergistic effect. MAO-AIs significantly reduced wound closure and invasion ability in breast cancer cells. Also, MAO-AIs reduced the colony count and size of breast cancer cells. MAO-AIs resulted in significant proapoptotic activity in breast cancer cells. Finally, the MAO-AIs suppressed MAO-A, Bcl-2, and VEGF gene expressions in breast cancer cells relative to untreated cells. This study provides solid evidence supporting the anticancer effect of MAO-A inhibitors in breast cancer cells.

Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor.

Drug combination therapy has exhibited remarkable therapeutic efficacy and has gradually become a promising clinical treatment strategy of complex diseases such as cancers. As the related databases keep expanding, computational methods based on deep learning model have become powerful tools to predict synergistic drug combinations. However, predicting effective synergistic drug combinations is still a challenge due to the high complexity of drug combinations, the lack of biological interpretability, and the large discrepancy in the response of drug combinations in vivo and in vitro biological systems. Here, we propose DGSSynADR, a new deep learning method based on global structured features of drugs and targets for predicting synergistic anticancer drug combinations. DGSSynADR constructs a heterogeneous graph by integrating the drug-drug, drug-target, protein-protein interactions and multi-omics data, utilizes a low-rank global attention (LRGA) model to perform global weighted aggregation of graph nodes and learn the global structured features of drugs and targets, and then feeds the embedded features into a bilinear predictor to predict the synergy scores of drug combinations in different cancer cell lines. Specifically, LRGA network brings better model generalization ability, and effectively reduces the complexity of graph computation. The bilinear predictor facilitates the dimension transformation of the features and fuses the feature representation of the two drugs to improve the prediction performance. The loss function Smooth L1 effectively avoids gradient explosion, contributing to better model convergence. To validate the performance of DGSSynADR, we compare it with seven competitive methods. The comparison results demonstrate that DGSSynADR achieves better performance. Meanwhile, the prediction of DGSSynADR is validated by previous findings in case studies. Furthermore, detailed ablation studies indicate that the one-hot coding drug feature, LRGA model and bilinear predictor play a key role in improving the prediction performance. DGSSynADR is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/DGSSynADR. Supplementary data are available at Bioinformatics online.

Effect of apatinib on the pharmacokinetics of tramadol and O-desmethyltramadol in rats.

Since the combination of anticancer drugs and opioids is very common, apatinib and tramadol are likely to be used in combination clinically. This study evaluated the effects of apatinib on the pharmacokinetics of tramadol and its main metabolite O-desmethyltramadol in Sprague-Dawley (SD) rats and the inhibitory effects of apatinib on tramadol in rat liver microsomes (RLMs), human liver microsomes (HLMs) and recombinant human CYP2D6.1. The samples were determined by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The in vivo results showed that compared with the control group, apatinib increased the AUC(0-t), AUC(0-∞) and Cmax values of tramadol and O-desmethyltramadol, and decreased the values of VZ/F and CLz/F. In addition, the MRT(0-t), MRT(0-∞) values of O-desmethyltramadol were increased. In vitro, apatinib inhibited the metabolism of tramadol by a mixed way with IC50 of 1.927 µM in RLMs, 2.039 µM in HLMs and 15.32 µM in CYP2D6.1. In summary, according to our findings, apatinib has a strong in vitro inhibitory effect on tramadol, and apatinib can increase the analgesic effect of tramadol and O-desmethyltramadol in rats.

Organoids and metastatic orthotopic mouse model for mismatch repair-deficient colorectal cancer.

Genome integrity is essential for the survival of an organism. DNA mismatch repair (MMR) genes (e.g., MLH1, MSH2, MSH6, and PMS2) play a critical role in the DNA damage response pathway for genome integrity maintenance. Germline mutations of MMR genes can lead to Lynch syndrome or constitutional mismatch repair deficiency syndrome, resulting in an increased lifetime risk of developing cancer characterized by high microsatellite instability (MSI-H) and high mutation burden. Although immunotherapy has been approved for MMR-deficient (MMRd) cancer patients, the overall response rate needs to be improved and other management options are needed. To better understand the biology of MMRd cancers, elucidate the resistance mechanisms to immune modulation, and develop vaccines and therapeutic testing platforms for this high-risk population, we generated organoids and an orthotopic mouse model from intestine tumors developed in a Msh2-deficient mouse model, and followed with a detailed characterization. The organoids were shown to be of epithelial origin with stem cell features, to have a high frameshift mutation frequency with MSI-H and chromosome instability, and intra- and inter-tumor heterogeneity. An orthotopic model using intra-cecal implantation of tumor fragments derived from organoids showed progressive tumor growth, resulting in the development of adenocarcinomas mixed with mucinous features and distant metastasis in liver and lymph node. The established organoids with characteristics of MSI-H cancers can be used to study MMRd cancer biology. The orthotopic model, with its distant metastasis and expressing frameshift peptides, is suitable for evaluating the efficacy of neoantigen-based vaccines or anticancer drugs in combination with other therapies.

AttenSyn: An Attention-Based Deep Graph Neural Network for Anticancer Synergistic Drug Combination Prediction.

Identifying synergistic drug combinations is fundamentally important to treat a variety of complex diseases while avoiding severe adverse drug-drug interactions. Although several computational methods have been proposed, they highly rely on handcrafted feature engineering and cannot learn better interactive information between drug pairs, easily resulting in relatively low performance. Recently, deep-learning methods, especially graph neural networks, have been widely developed in this area and demonstrated their ability to address complex biological problems. In this study, we proposed AttenSyn, an attention-based deep graph neural network for accurately predicting synergistic drug combinations. In particular, we adopted a graph neural network module to extract high-latent features based on the molecular graphs only and exploited the attention-based pooling module to learn interactive information between drug pairs to strengthen the representations of drug pairs. Comparative results on the benchmark datasets demonstrated that our AttenSyn performs better than the state-of-the-art methods in the prediction of anticancer synergistic drug combinations. Additionally, to provide good interpretability of our model, we explored and visualized some crucial substructures in drugs through attention mechanisms. Furthermore, we also verified the effectiveness of our proposed AttenSyn on two cell lines by visualizing the features of drug combinations learnt from our model, exhibiting satisfactory generalization ability.