Combination Of Anticancer Drugs Research Articles

Corrigendum: Network-based prediction of anti-cancer drug combinations

Corrigendum: Network-based prediction of anti-cancer drug combinations

DRUG UTILIZATION PATTERN IN INDOOR PATIENT OF RADIOTHERAPY DEPARTMENT AT A TERTIARY CARE HOSPITAL IN SOUTHERN RAJASTHAN

Objective: The study was planned to evaluate on drug utilization pattern of drug use in the indoor of radiotherapy department, using the World Health Organization recommended core drug use indicators, to compare the cost of drug used and to provide feedback to the concerned department to improve rational prescribing. Methods: Four hundred and five newly diagnosed cases of cancer with age more than 18 of both genders were included in the study. The demographic data, diagnosis, group of anticancer drugs, individual anticancer drug, combinations of anticancer drugs, and cost of drug reactions were recorded and analyzed statistically, using descriptive study statistics. Results: Cervical cancer (19.51%) was the most frequently diagnosed cancer, followed by lung cancer (17.04%). Platinum compounds (76.30%) were maximum utilized group of drugs. Carboplatin was the most commonly prescribed drug (53.09). Average number of drugs prescribed per prescription was 8.77. Carboplatin + Paclitaxel combination was the most commonly prescribed combination. Pantoprazole was maximally used adjuvant drug. Conclusion: About 100% of drugs were prescribed by generic name and 86.27% of drugs were prescribed from National List of Essential Medicines of India, 2022 indicates rational prescribing. The cost of anticancer drugs was much less in comparison to other studies.

Boron Salicylate Ester Compounds as Boron Therapeutics. Their Synthesis, Structural Characterizations and Anticancer Effects against MDA-MB-231.

The element boron forms a wide range of borate minerals with different properties. Borate minerals make it possible to design boron-containing molecules with new biological properties in terms of their chemical structure and properties. It is known that boron compounds have antioxidant, anti-inflammatory, anti-tumor and anti-cancer properties. This makes boron compounds important for the future development of boron chemotherapeutics, boron supplements and new drugs. Reliable scientific studies on boron compounds will facilitate the clear presentation of their functions in its biological applications and metabolism. In this study, boron monoester and boron diester structures were synthesized with salicylic acid ligand. To stabilize boron ester structures, Na+, K+, Mg2+, Ca2+ cations were used as counter-ions. Structural properties of the synthesized substances, molecules obtained by crystallization/precipitation from aqueous solutions in solid state, elemental analysis, melting point determination, infrared spectroscopy analysis (FT-IR), thermal analysis (TGA/DTA), mass analysis (GC-MS) and single crystal analysis. Structural properties were tried to be explained by structure analysis (SC-XRD) methods. Additionally, the anticancer potential of boron salicylate esters against the MDA-MB-231 human breast adenocarcinoma cell line was examined. The K-B salicylate diester molecule was found to have the most potential potency with the lowest IC50 value against the MDA-MB-231 cell line. The anticancer potential of boron salicylate esters can be further investigated with other cancer models with the combination of anticancer drugs. It is also thought that the mechanism of action of these molecules may help reveal their further applications.

A common druggable signature of oncogenic c-Myc, mutant KRAS and mutant p53 reveals functional redundancy and competition among oncogenes in cancer

The major driver oncogenes MYC, mutant KRAS, and mutant TP53 often coexist and cooperate to promote human neoplasia, which results in anticancer therapeutic opportunities within their downstream molecular programs. However, little research has been conducted on whether redundancy and competition among oncogenes affect their programs and ability to drive neoplasia. By CRISPR‒Cas9-mediated downregulation we evaluated the downstream proteomics and transcriptomics programs of MYC, mutant KRAS, and mutant TP53 in a panel of cell lines with either one or three of these oncogenes activated, in cancers of the lung, colon and pancreas. Using RNAi screening of the commonly activated molecular programs, we found a signature of three proteins - RUVBL1, HSPA9, and XPO1, which could be efficiently targeted by novel drug combinations in the studied cancer types. Interestingly, the signature was controlled by the oncoproteins in a redundant or competitive manner rather than by cooperation. Each oncoprotein individually upregulated the target genes, while upon oncogene co-expression each target was controlled preferably by a dominant oncoprotein which reduced the influence of the others. This interplay was mediated by redundant routes of target gene activation - as in the case of mutant KRAS signaling to c-Jun/GLI2 transcription factors bypassing c-Myc activation, and by competition - as in the case of mutant p53 and c-Myc competing for binding to target promoters. The global transcriptomics data from the cell lines and patient samples indicate that the redundancy and competition of oncogenic programs are broad phenomena, that may constitute even a majority of the genes dependent on oncoproteins, as shown for mutant p53 in colon and lung cancer cell lines. Nevertheless, we demonstrated that redundant oncogene programs harbor targets for efficient anticancer drug combinations, bypassing the limitations for direct oncoprotein inhibition.

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.

PiscesCSM: prediction of anticancer synergistic drug combinations

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 contributionThis 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.

Molecular Hydrogen Protects against Various Tissue Injuries from Side Effects of Anticancer Drugs by Reducing Oxidative Stress and Inflammation.

While drug therapy plays a crucial role in cancer treatment, many anticancer drugs, particularly cytotoxic and molecular-targeted drugs, cause severe side effects, which often limit the dosage of these drugs. Efforts have been made to alleviate these side effects by developing derivatives, analogues, and liposome formulations of existing anticancer drugs and by combining anticancer drugs with substances that reduce side effects. However, these approaches have not been sufficiently effective in reducing side effects. Molecular hydrogen (H2) has shown promise in this regard. It directly reduces reactive oxygen species, which have very strong oxidative capacity, and indirectly exerts antioxidant, anti-inflammatory, and anti-apoptotic effects by regulating gene expression. Its clinical application in various diseases has been expanded worldwide. Although H2 has been reported to reduce the side effects of anticancer drugs in animal studies and clinical trials, the underlying molecular mechanisms remain unclear. Our comprehensive literature review revealed that H2 protects against tissue injuries induced by cisplatin, oxaliplatin, doxorubicin, bleomycin, and gefitinib. The underlying mechanisms involve reductions in oxidative stress and inflammation. H2 itself exhibits anticancer activity. Therefore, the combination of H2 and anticancer drugs has the potential to reduce the side effects of anticancer drugs and enhance their anticancer activities. This is an exciting prospect for future cancer treatments.

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.

The Anticancer Application of Delivery Systems for Honokiol and Magnolol.

Cancer is a leading cause of death worldwide, and the effectiveness of treatment is consistently not at a satisfactory level. This review thoroughly examines the present knowledge and perspectives of honokiol (HON) in cancer therapeutics. The paper synthesizes critical insights into the molecular mechanisms underlying the observed anticancer effects, emphasizing both in vitro and in vivo studies. The effects of HON application, primarily in the common types of cancers, are presented. Because the therapeutic potential of HON may be limited by its physicochemical properties, appropriate delivery systems are sought to overcome this problem. This review discusses the effect of different nanotechnology-based delivery systems on the efficiency of HON. The data presented show that HON exhibits anticancer effects and can be successfully administered to the site of action. Honokiol exerts its anticancer activity through several mechanisms. Moreover, some authors used the combinations of classical anticancer drugs with HON. Such an approach is very interesting and worth further investigation. Understanding HON's multiple molecular mechanisms would provide valuable insights into how HON might be developed as an effective therapeutic. Therefore, further research is needed to explore its specific applications and optimize its efficacy in diverse cancer types.

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.

Network-based prediction of anti-cancer drug combinations.

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.

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.