Synergistic Drug Combinations Research Articles

Unlocking the therapeutic potential of drug combinations through synergy prediction using graph transformer networks

Drug combinations are frequently used to treat cancer to reduce side effects and increase efficacy. The experimental discovery of drug combination synergy is time-consuming and expensive for large datasets. Therefore, an efficient and reliable computational approach is required to investigate these drug combinations. Advancements in deep learning can handle large datasets with various biological problems. In this study, we developed a SynergyGTN model based on the Graph Transformer Network to predict the synergistic drug combinations against an untreated cancer cell line expression profile. We represent the drug via a graph, with each node and edge of the graph containing nine types of atomic feature vectors and four bonds features, respectively. The cell lines represent based on their gene expression profiles. The drug graph was passed through the GTN layers to extract a generalized feature map for each drug pairs. The drug pair extracted features and cell-line gene expression profiles were concatenated and subsequently subjected to processing through multiple densely connected layers. SynergyGTN outperformed the state-of-the-art methods, with a receiver operating characteristic area under the curve improvement of 5% on the 5-fold cross-validation. The accuracy of SynergyGTN was further verified through three types of cross-validation tests strategies namely leave-drug-out, leave-combination-out, and leave-tissue-out, resulting in improvement in accuracy of 8%, 1%, and 2%, respectively. The Astrazeneca Dream dataset was utilized as an independent dataset to validate and assess the generalizability of the proposed method, resulting in an improvement in balanced accuracy of 13%. In conclusion, SynergyGTN is a reliable and efficient computational approach for predicting drug combination synergy in cancer treatment. Finally, we developed a web server tool to facilitate the pharmaceutical industry and researchers, as available at: http://nsclbio.jbnu.ac.kr/tools/SynergyGTN/.

A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer.

The exploration of drug combinations presents an opportunity to amplify therapeutic effectiveness while alleviating undesirable side effects. Nevertheless, the extensive array of potential combinations poses challenges in terms of cost and time constraints for experimental screening. Thus, it is crucial to narrow down the search space. Deep learning approaches have gained widespread popularity in predicting synergistic drug combinations tailored for specific cell lines in vitro settings. In the present study, we introduce a novel method termed GTextSyn, which utilizes the integration of gene expression data and chemical structure information for the prediction of synergistic effects in drug combinations. GTextSyn employs a sentence classification model within the domain of Natural Language Processing (NLP), wherein drugs and cell lines are regarded as entities possessing biochemical relevance. Meanwhile, combinations of drug pairs and cell lines are construed as sentences with biochemical relational significance. To assess the efficacy of GTextSyn, we conduct a comparative analysis with alternative deep learning approaches using a standard benchmark dataset. The results from a five-fold cross-validation demonstrate a 49.5% reduction in Mean Square Error (MSE) achieved by GTextSyn, surpassing the performance of the next best method in the regression task. Furthermore, we conduct a comprehensive literature survey on the predicted novel drug combinations and find substantial support from prior experimental studies for many of the combinations identified by GTextSyn.

Predicting drug synergy using a network propagation inspired machine learning framework.

Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability. Here, we proposed a novel network propagation-based machine learning framework to predict synergistic drug combinations. Based on the topological information of a comprehensive drug-drug association network, we innovatively introduced an affinity score between drug pairs as one of the features to train machine learning models. We applied network-based strategy to evaluate their therapeutic potential to different cancer types. Finally, we identified 17 specific-, 21 general- and 40 broad-spectrum antitumor drug combinations, in which 69% drug combinations were validated by vitro cellular experiments, 83% drug combinations were validated by literature reports and 100% drug combinations were validated by biological function analyses. By quantifying the network relationships between drug targets and cancer-related driver genes in the human protein-protein interactome, we show the existence of four distinct patterns of drug-drug-disease relationships. We also revealed that 32 biological pathways were correlated with the synergistic mechanism of broad-spectrum antitumor drug combinations. Overall, our model offers a powerful scalable screening framework for cancer treatments.

Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool

PurposeAntimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Learning Synergy Predictor (MLSyPred©), to predict potential synergy in antimalarial drug combinations.MethodsThe MLSyPred© synergy prediction method extracts molecular fingerprints from the drugs’ biochemical structures to use as features and also cleans and prepares the raw data. Five machine learning algorithms (Logistic Regression, Random Forest, Support vector machine, Ada Boost, and Gradient Boost) were implemented to build prediction models. Implementation and application of the MLSyPred© tool were tested using datasets from 1540 combinations of 79 drugs and compounds biologically evaluated in pairs for three strains of Plasmodium falciparum (3D7, HB3, and Dd2).ResultsThe best prediction models were obtained using Logistic Regression for antimalarials with the strains Dd2 and HB3 (0.81 and 0.70 AUC, respectively) and Random Forest for antimalarials with 3D7 (0.69 AUC). The MLSyPred© tool yielded 45% precision for synergistically predicted antimalarial drug combinations that were annotated and biologically validated, thus confirming the functionality and applicability of the tool.Conclusion The MLSyPred© tool is freely available and represents a promising strategy for discovering potential synergistic drug combinations for further development as novel antimalarial therapies.

Synergistic celecoxib and dimethyl-celecoxib combinations block cervix cancer growth through multiple mechanisms.

The synergistic inhibitory effect of celecoxib (CXB) and dimethyl-celecoxib (DMC) plus paclitaxel (PA) or cisplatin (CP) on human cervix HeLa and SiHa cells was assessed at multiple cellular levels in order to elucidate the biochemical mechanisms triggered by the synergistic drug combinations. The effect of CXB (5 μM)/CP (2 μM) or CXB (5 μM)/PA (15 μM) and DMC (15 μM)/CP (5 μM) or DMC (15 μM)/PA (20 μM) for 24 h was assayed on cancer cell proliferation, energy metabolism, mitophagy, ROS production, glycoprotein-P activity, DNA stability and apoptosis/necrosis. Drug combinations synergistically decreased HeLa and SiHa cell proliferation (>75%) and arrested cellular cycle by decreasing S and G2/M phases as well as the Ki67 content (HeLa) by 7.5-30 times. Cell viability was preserved (>90%) and no apparent effects on non-cancer cell growth were observed. Mitochondrial and glycolytic protein contents (44-95%) and ΔΨm (45-50%) in HeLa cells and oxidative phosphorylation and glycolysis fluxes (70-90%) in HeLa and SiHa cells were severely decreased, which in turn promoted a drastic fall in the ATP supply (85-88%). High levels of mitophagy proteins in HeLa cells and active mitochondrial digestion in HeLa and SiHa cells was observed. Mitochondrial fission and microtubule proteins were also affected. Intracellular ROS content (2-2.3-fold) and ROS production was stimulated (2.3-4 times), whereas content and activity of glycoprotein-P (45-85%) were diminished. DNA fragmentation was not observed and apoptosis/necrosis was not detected suggesting that cell death could be mainly associated to mitophagy induction. CXB or DMC combination with canonical chemotherapy may be a promising chemotherapy strategy against cervical cancer growth, because it can selectively block multiple cell processes including inhibition of energy pathways and in consequence ATP-dependent processes such as cell proliferation, glycoprotein-P activity, ROS production and mitophagy, with no apparent effects on non-cancer cells.

MDNNSyn: A Multi-Modal Deep Learning Framework for Drug Synergy Prediction.

Synergistic drug combination prediction tasks based on the computational models have been widely studied and applied in the cancer field. However, most of models only consider the interactions between drug pairs and specific cell lines, without taking into account the multiple biological relationships of drug-drug and cell line-cell line that also largely affect synergistic mechanisms. To this end, here we propose a multi-modal deep learning framework, termed MDNNSyn, which adequately applies multi-source information and trains multi-modal features to infer potential synergistic drug combinations. MDNNSyn extracts topology modality features by implementing the multi-layer hypergraph neural network on drug synergy hypergraph and constructs semantic modality features through similarity strategy. A multi-modal fusion network layer with gated neural network is then employed for synergy score prediction. MDNNSyn is compared to five classic and state-of-the-art prediction methods on DrugCombDB and Oncology-Screen datasets. The model achieves area under the curve (AUC) scores of 0.8682 and 0.9013 on two datasets, an improvement of 3.70 % and 2.71 % over the second-best model. Case study indicates that MDNNSyn is capable of detecting potential synergistic drug combinations.

ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery.

Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations.

Ex vivo discovery of synergistic drug combinations for hematologic malignancies

Combination therapies have improved outcomes for patients with acute myeloid leukemia (AML). However, these patients still have poor overall survival. Although many combination therapies are identified with high-throughput screening (HTS), these approaches are constrained to disease models that can be grown in large volumes (e.g., immortalized cell lines), which have limited translational utility. To identify more effective and personalized treatments, we need better strategies for screening and exploring potential combination therapies. Our objective was to develop an HTS platform for identifying effective combination therapies with highly translatable ex vivo disease models that use size-limited, primary samples from patients with leukemia (AML and myelodysplastic syndrome). We developed a system, ComboFlow, that comprises three main components: MiniFlow, ComboPooler, and AutoGater. MiniFlow conducts ex vivo drug screening with a miniaturized flow-cytometry assay that uses minimal amounts of patient sample to maximize throughput. ComboPooler incorporates computational methods to design efficient screens of pooled drug combinations. AutoGater is an automated gating classifier for flow cytometry that uses machine learning to rapidly analyze the large datasets generated by the assay. We used ComboFlow to efficiently screen more than 3000 drug combinations across 20 patient samples using only 6 million cells per patient sample. In this screen, ComboFlow identified the known synergistic combination of bortezomib and panobinostat. ComboFlow also identified a novel drug combination, dactinomycin and fludarabine, that synergistically killed leukemic cells in 35 % of AML samples. This combination also had limited effects in normal, hematopoietic progenitors. In conclusion, ComboFlow enables exploration of massive landscapes of drug combinations that were previously inaccessible in ex vivo models. We envision that ComboFlow can be used to discover more effective and personalized combination therapies for cancers amenable to ex vivo models.

In situ modeling of acquired resistance to RTK/RAS-pathway-targeted therapies

Intrinsic and acquired resistance limit the window of effectiveness for oncogene-targeted cancer therapies. Here, we describe an in situ resistance assay (ISRA) that reliably models acquired resistance to RTK/RAS-pathway-targeted therapies across cell lines. Using osimertinib resistance in EGFR-mutated lung adenocarcinoma (LUAD) as a model system, we show that acquired osimertinib resistance can be significantly delayed by inhibition of proximal RTK signaling using SHP2 inhibitors. Isolated osimertinib-resistant populations required SHP2 inhibition to resensitize cells to osimertinib and reduce MAPK signaling to block the effects of enhanced activation of multiple parallel RTKs. We additionally modeled resistance to targeted therapies including the KRASG12C inhibitors adagrasib and sotorasib, the MEK inhibitor trametinib, and the farnesyl transferase inhibitor tipifarnib. These studies highlight the tractability of in situ resistance assays to model acquired resistance to targeted therapies and provide a framework for assessing the extent to which synergistic drug combinations can target acquired drug resistance.

Abstract B171: BX-795 enhances the efficacy of crizotinib in colorectal cancer cells by altering the activity of aurora kinases

Abstract Background: Metastatic Colorectal cancer (mCRC) is driven by various genetic mutations that, in turn, present potentially actionable targets. Recent improvements in combining therapies have shown modest benefits in patients with BRAF mutated mCRC. Several efforts focusing on various KRAS inhibitors are ongoing in mCRC with outcomes pending. With no significant advances to improve outcomes in most patients with mCRC, it is important to identify novel therapies that have the potential to significantly improve the outcomes of these patients. Aims: To perform high-throughput screening (HTS) using clinically relevant drugs to identify novel and effective therapeutic combinations for patients with KRAS/BRAF mutated mCRC. Methods: We performed unbiased HTS with ~250 drugs that were either currently in clinical trials or approved by the FDA. 57 drugs that decreased cell proliferation in both 2D and 3D cultures and were effective in both HCT116 (KRAS mutated) and HT29 (BRAF mutated) cell lines, were termed as “pan-active” drugs. These drugs were divided into 9 classes based on targets they inhibit. Using the BLISS model, synergistic drug combinations were identified by HTS where the top three effective drugs from each class of “pan-active” drugs were paired with the top three drugs of all other classes. Effects of one such combination (BX-795 with crizotinib) were further validated in vitro by cell growth assays and measuring changes in apoptotic mediators by western blotting (WB) and flow cytometry (FACS). Mechanisms of synergy were examined by Reverse Phase Protein Array (RPPA) followed by WB and immunostaining of CRC cells to assess inhibition of downstream targets. Results: Pairwise HTS studies identified the PDK1/TBK1 inhibitor BX-795 to be synergistic with the MET/ALK inhibitor crizotinib in multiple CRC cell lines. This combination was further validated in vitro by MTT and colony formation assays in various KRAS and BRAF mutated CRC cell lines. Analyses of markers for apoptosis by WB and FACS using FITC-Annexin V and PI confirmed that the drug combination increased cell death in multiple CRC cell lines as compared to single agents. Unbiased RPPA indicated that the combination led to very strong and unexpected inhibition of the Aurora kinases along with decreases in the expected AKT/mTOR pathway in multiple CRC cell lines when compared to monotherapies. Immunostaining revealed significant mitotic defects in CRC cells treated with the drug combination but not with single agents suggesting that mitotic defects was a possible mechanism of cell death. Further, combining an Aurora kinase A inhibitor with crizotinib enhanced the efficacy of crizotinib validating the proposed mechanism. Conclusions: Our unbiased HTS and validation studies demonstrated that combining BX-795 with crizotinib is a potential novel effective therapeutic combination for patients with mCRC. Our in vitro studies serve as the basis for future preclinical and, if warranted, clinical studies to determine the efficacy of this drug combination in patients with mCRC. Citation Format: Susmita Ghosh, Fan Fan, Reid Powell, Yong Sung Park, Clifford Stephan, Jason Roszik, Lee M Ellis, Rajat Bhattacharya. BX-795 enhances the efficacy of crizotinib in colorectal cancer cells by altering the activity of aurora kinases [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B171.

Phenotypic Drug Response Profiling Identifies Asparaginase-Based Synergistic Combinations for Very High Risk Acute Lymphoblastic Leukaemia

In acute lymphoblastic leukaemia (ALL), empirical multi-drug chemotherapy combinations lead to >85% cure rates in children. But 10-15% of patients show suboptimal response and as drugs are dosed to tolerance, further intensification is not possible. An exception is the key antileukaemic drug, asparaginase (ASNase), which is not associated with dose-related toxicity. We evaluated the use of ex-vivo co-culture based drug response profiling (DRP) to identify novel synergistic and effective drug combinations with ASNase for patients with suboptimal responses to standard chemotherapy in ALL. Patient-derived xenograft (PDX) ALL cells from 5 patients with high-risk B-ALL (3 B-other, 2 IKZF plus) were treated with 120 drugs in 5-point serial dilutions in combination with or without an IC 50 dose of ASNase, in co-cultures with hTERT-immortalized primary bone marrow stromal cells. Eight clinically relevant agents with promising sensitizing activity as identified by decreased area-under-the-curve with addition of ASNase were identified (SEL, selinexor; ELTA, eltanexor; VEN, venetoclax; BZM, bortezomib; navitoclax; carfilzomib; mitoxantrone, birinapant). We validated combinations of ASNase with the 8 selected drugs plus 4 drugs used in induction therapy (prednisolone, dexamethasone, vincristine and daunorubicin) using a 4x4 drug matrix in 15 high-risk ALL PDX samples. Synergy scores were calculated using the zero-interaction potency (ZIP) model. Synergy (ZIP score ≥10) or additivity (ZIP score >0 and <10) with ASNase was identified for exportin-1 inhibitors (XPO1i; selinexor, eltanexor), venetoclax and proteosomal inhibitors (bortezomib, carfilzomib) in 100% (15/15), 86.6% (13/15) and 80-86.6% (12/15 and 13/15) samples respectively. Antagonism (ZIP score <0) was noted in more than one-third (6/15) of samples with ASNase in combination with steroids or vincristine. Three-drug combinations were evaluated ex-vivo in these 15 PDX samples. Synergy was identified with addition of low doses of BZM or VEN to the ASNase-SEL combination (ASNase-SEL-BZM and ASNase-SEL-VEN). Next, 4-6 mice per treatment arm were transplanted intravenously with 1 million ALL cells from 4 high-risk B-ALL patients. Randomized cohorts were treated with vehicle, ASNase-BZM-ELTA (ABE) and ASNase-dexamethasone-vincristine (ADV). Leukemic burden was assessed weekly using flowcytometry. The mice tolerated both treatment arms well. Platelet counts recovered within one week after an initial drop in the ABE arm. Both treatment groups had significant improvement in event free survival (EFS) as compared to the control group (ABE p-value 0.0082; ADV p-value 0.0325) though no significant differences were noted between the 2 treatment arms (p-value 0.8971). Three patients with high-risk medullary relapsed/refractory BCP-ALL, 2 not in morphological remission and 1 at time of very early relapse, received a combination of ASNase-BZM-VEN-SEL (ABVS) after consent. One patient for whom DRP suggested sensitivity to both SEL and VEN achieved molecular remission (molecular minimal residual disease < 10 -5) after one course of therapy remaining in complete remission 11 months later. Two patients with sensitivity to SEL but not to VEN as assessed by DRP, had progressive disease. ABVS was well tolerated in these patients with no grade 3-4 adverse events. Our results identify XPO1 inhibitors as a new class of drugs with promising anti-leukaemic activity in BCP-ALL. Co-culture based DRP shows promise in identifying alternative novel sensitive synergistic combinations ex-vivo with decreased toxicity for patients who have an inadequate response to standard therapy.

Therapeutic Targeting of JAK/STAT Signaling and Histone Acetyltransferase Activity in Post-Myeloproliferative Neoplasm Secondary AML

Post-myeloproliferative neoplasm secondary Acute Myeloid Leukemia (Post-MPN sAML) is an aggressive and lethal hematologic malignancy arising from myeloproliferative neoplasms (MPN). Hyperactivation of JAK/STAT signaling is present in 50-90% of MPNs, underscoring its role in driving MPN pathogenesis and transformation to acute sAML. Ruxolitinib, a Type-I JAK2 inhibitor approved for the treatment of myelofibrosis, was tested in a Phase II clinical trial in post-MPN sAML. Despite enhancing patients' quality of life, ruxolitinib did not exert major improvements on disease outcome (Eghtedar et al, 2012). Thus, there is an urgent need to find synergistic drug combinations capable of complementing the therapeutic activity of JAK2 inhibitors in post-MPN sAML. Here, we demonstrate that combination treatment of ruxolitinib and CBP30, a bromodomain inhibitor of histone acetyltransferases (HAT) CREBBP and p300, enhances the therapeutic activity of ruxolitinib in post-MPN sAML using high throughput genetic screens, in vitro functional assays, transcriptomic and epigenetic profiling and mouse xenograft in vivo experiments. To identify druggable determinants of JAK2 inhibitor response in post-MPN sAML, we performed genome-wide loss-of-function CRISPR screens in sAML HEL cells treated with four different JAK2 inhibitors, including ruxolitinib. We discovered that depletion of the histone acetyl transferase CREBBP sensitizes HEL cells to JAK2 inhibition (Figure 1A). Pharmacological inhibition of JAK/STAT signaling and HAT activity using a combination of ruxolitinib and CREBBP inhibitors significantly reduced the proliferation of sAML cells HEL and SET2 (Figure 1B). Among the CREBBP inhibitors, CBP30 synergizes best with ruxolitinib having Chou-Talalay combination indices of 0.152 and 0.704 in HEL and SET2, respectively. Moreover, combination treatment of ruxolitinib and CBP30 markedly increased apoptosis and induced cell cycle arrest at G1 in HEL. To mechanistically characterize the synergism between ruxolitinib and CBP30, we performed ChIP-seq to profile STAT5 binding and H3K27ac levels in HEL cells treated with DMSO, ruxolitinib, CBP30 and their combination. STAT5 binding profiles did not reveal major differences in ruxolitinib versus ruxolitinib + CBP30 treated cells, suggesting that the drug combination does not significantly alter STAT5 binding. Conversely, we noticed striking global changes in H3K27ac levels in the combination compared with DMSO or single agents, indicating that the synergistic effect between ruxolitinib and CBP30 is mediated by substantial changes in transcriptional regulation. Notably, analysis of H3K27ac downregulated and upregulated sites identified enrichments in motifs recognized by FOS/SMAD4 and GATA transcription factors, respectively. These TFs are involved in the regulation of myeloid differentiation implicating a synergistic regulation of relevant myeloid differentiation pathways by the combination of JAK2 and CREBBP inhibitors in sAML. Furthermore, ruxolitinib + CBP30 treatment in HEL elicits a unique transcriptome profile with expression deregulation of genes associated with cell cycle, DNA repair, HAT activity and TGF-beta signaling as determined by DAVID functional annotation analysis. Lastly, to evaluate the synergistic effects of ruxolitinib and CBP30 in vivo, we treated luciferase-expressing HEL sAML mouse xenografts with vehicle, ruxolitinib (80 mg/kg), CBP30 (30 mg/kg) and ruxolitinib (80 mg/kg) + CBP30 (30 mg/kg). Combination of ruxolitinib and CBP30 displayed significant decrease in leukemia burden after 4 weeks of treatment as monitored by bioluminescence imaging and quantification. Consistently, mice treated with both drugs also showed remarkable decrease in their white blood cell count and spleen size compared with single-agent-treated and vehicle-treated mice. Collectively, our results substantiate the combinatorial therapeutic targeting of JAK/STAT signaling and HAT activity as a potential treatment approach for post-MPN sAML.

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.

Combined Treatment with Host-Directed and Anticytomegaloviral Kinase Inhibitors: Mechanisms, Synergisms and Drug Resistance Barriers.

Despite the availability of currently approved antiviral drugs, infections with human cytomegalovirus (HCMV) still cause clinically challenging, sometimes life-threatening situations. There is an urgent need for enhanced anti-HCMV drugs that offer improved efficacy, reduced dosages and options for long-term treatment without risk of the development of viral drug resistance. Recently, we reported the pronounced anti-HCMV efficacy of pharmacological inhibitors of cyclin-dependent kinases (CDKs), in particular, the potential of utilizing drug synergies upon combination treatment with inhibitors of host CDKs and the viral CDK-like kinase pUL97 (vCDK/pUL97). Here, we expand this finding by further assessing the in vitro synergistic antiviral interaction between vCDK and CDK inhibitors towards HCMV as well as non-human cytomegaloviruses. An extension of this synergy approach was achieved in vivo by using the recombinant MCMV-UL97/mouse model, confirming the high potential of combination treatment with the clinically approved vCDK inhibitor maribavir (MBV) and the developmental CDK7 inhibitor LDC4297. Moreover, mechanistic aspects of this synergistic drug combination were illustrated on the levels of intracellular viral protein transport and viral genome replication. The analysis of viral drug resistance did not reveal resistance formation in the case of MBV + LDC4297 combination treatment. Spanning various investigational levels, these new results strongly support our concept, employing the great potential of anti-HCMV synergistic drug treatment.

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.

Enhancing anti-cancer potential by delivering synergistic drug combinations via phenylboronic acid modified PLGA nanoparticles through ferroptosis-based therapy

In this study, we investigated the potential of the sorafenib (SOR) and simvastatin (SIM) combination to induce ferroptosis-mediated cancer therapy. To enhance targeted drug delivery, we encapsulated the SOR + SIM combination within 4-carboxy phenylboronic acid (CPBA) modified PLGA nanoparticles (CPBA-PLGA(SOR + SIM)-NPs). The developed CPBA-PLGA(SOR + SIM)-NPs exhibited a spherical shape with a size of 213.1 ± 10.9 nm, a PDI of 0.22 ± 0.03, and a Z-potential of −22.9 ± 3.2 mV. Notably, these nanoparticles displayed faster drug release at acidic pH compared to physiological pH. In cellular experiments, CPBA-PLGA(SOR + SIM)-NPs demonstrated remarkable improvements, leading to a 2.51, 2.69, and 2.61-fold decrease in IC50 compared to SOR alone, and a 7.50, 16.71, and 5.11-fold decrease in IC50 compared to SIM alone in MDA-MB-231, A549, and HeLa cells, respectively. Furthermore, CPBA-PLGA(SOR + SIM)-NPs triggered a reduction in glutathione (GSH) levels, an increase in malondialdehyde (MDA) levels, and mitochondrial membrane depolarization in all three cell lines. Pharmacokinetic evaluation revealed a 2.50- and 2.63-fold increase in AUC0-∞, as well as a 1.53- and 2.46-fold increase in mean residence time (MRT) for SOR and SIM, respectively, compared to the free drug groups. Notably, the CPBA-PLGA(SOR + SIM)-NPs group exhibited significant reduction in tumor volume, approximately 9.17, 2.45, and 1.63-fold lower than the control, SOR + SIM, and PLGA(SOR + SIM)-NPs groups, respectively. Histological examination and biomarker analysis showed no significant differences compared to the control group, suggesting the biocompatibility of the developed particles for in-vivo applications. Altogether, our findings demonstrate that CPBA-PLGA(SOR + SIM)-NPs hold tremendous potential as an efficient drug delivery system for inducing ferroptosis, providing a promising therapeutic option for cancer treatment.

EXTH-71. AUTOMATED AND MULTIPLEXED COMBINATORIAL DRUG SCREENING OF FDA-APPROVED DRUGS IDENTIFIES POTENTIAL SYNERGISTIC DRUG PARTNERS FOR THE TREATMENT OF AGGRESSIVE MENINGIOMAS

Abstract The goal of combinatorial therapy is to improve the effectiveness of treatment by targeting multiple aspects of cancer cells or by overcoming resistance to single drugs. In search of effective synergistic drug combinations for the treatment of aggressive meningiomas, we performed an automated and multiplexed combinatorial drug screening of 166 FDA-approved anticancer drugs in three grade 3 meningioma cell lines. Three anaplastic meningioma cell lines (NCH93, IOMM-Lee, and KT21-MG1) were stably transduced with blue, green, and red fluorescence proteins. The cell lines were multiplexed into 384-well plates and treated in 5x5 dose-response matrices (1 to 1000 nmol/L) for 48 hours by the automated liquid handler Hamilton MicroLAB STAR®. The drug library consisted of 166 FDA-approved anticancer drugs. To demultiplex cell viability from a single well, fluorescence as a surrogate marker for viability was measured before adding an ATP-based end-point viability assay, which served as an independent combined result of all cell lines (combined group). Synergism was calculated by the ZIP Synergy model and was defined as a ZIP score over 10. This drug screening effort generated 13695 unique drug-drug combinations per cell line. Most synergistic combinations were observed in the combined group (n = 188, 1.37%), followed by IOMM-Lee (n = 157, 1.14%), and KT21-MG1 (n = 122, 0.88%). The lowest number of synergistic combinations was observed in NCH93 (n = 75, 0.54%). Interestingly, 86 combinations were uniquely found in IOMM-Lee, 54 in KT21-MG1, and 20 in NCH93, indicating specific tumor vulnerabilities. 19 drug combinations were observed in all cell lines. Of those, the drugs Temsirolimus, Gemcitabine, and Romidepsin were highly enriched. For example, mTORi Temsirolimus concordantly showed strong synergism with the androgen receptor antagonist Darolutamide in all cell lines (ZIP score: 27.75, P< .0001). This comprehensive combinatorial drug screening identified synergistic drug-drug partners for the treatment of aggressive meningiomas. Validation studies in patient-derived meningioma organoids are ongoing.

Targeting multiple disease hallmarks using a synergistic disease-modifying drug combination ameliorates osteoarthritis via inhibition of senescence and inflammation

AimsOsteoarthritis (OA), is a debilitating disease characterized by progressive cartilage degradation, synovial inflammation, and chondrocyte senescence. Various treatment agents independently targeting these hallmarks have been investigated. However, due to the complex multifaceted nature of OA, no disease-modifying osteoarthritis drugs are clinically available. In an attempt to overcome this, we developed a combinatorial approach and demonstrated the efficacy of TsC [Tissue inhibitor of metalloproteinase-3 (TIMP3) + sulfated carboxymethylcellulose (sCMC)] and piperlongumine (PL) combination for the amelioration of OA in a goat ex vivo OA model. Main methodsThe efficacy of the drug combination was evaluated using the goat ex vivo OA explant model and results were validated in clinically relevant human OA cartilage explants. The chondroprotective effects were evaluated in terms of reduced inflammation and cartilage matrix loss, reduction in chondrosenescence, and reduced oxidative stress. Key findingsA combination of TsC and PL (TsC-PL) significantly reduced inflammation, cartilage matrix loss, chondrosenescence, and oxidative stress in the goat ex vivo OA model and showed chondroprotective effects. Further, similar chondroprotective effects were observed in human OA cartilage. Additionally, the coefficient of drug interaction analysis indicated that the combination of TsC and PL had a synergistic effect in reducing matrix degrading proteases and inflammation (goat ex vivo OA model) and Reactive oxygen species (ROS) production (human OA cartilage). SignificanceCombinatorial treatment with TsC and PL demonstrated potential disease-modifying effects for the treatment of osteoarthritis via inhibition of inflammation and senescence and supports the usage of treatment strategies targeting multiple pathological factors of OA simultaneously.

Signature-driven repurposing of Midostaurin for combination with MEK1/2 and KRASG12C inhibitors in lung cancer

Drug combinations are key to circumvent resistance mechanisms compromising response to single anti-cancer targeted therapies. The implementation of combinatorial approaches involving MEK1/2 or KRASG12C inhibitors in the context of KRAS-mutated lung cancers focuses fundamentally on targeting KRAS proximal activators or effectors. However, the antitumor effect is highly determined by compensatory mechanisms arising in defined cell types or tumor subgroups. A potential strategy to find drug combinations targeting a larger fraction of KRAS-mutated lung cancers may capitalize on the common, distal gene expression output elicited by oncogenic KRAS. By integrating a signature-driven drug repurposing approach with a pairwise pharmacological screen, here we show synergistic drug combinations consisting of multi-tyrosine kinase PKC inhibitors together with MEK1/2 or KRASG12C inhibitors. Such combinations elicit a cytotoxic response in both in vitro and in vivo models, which in part involves inhibition of the PKC inhibitor target AURKB. Proteome profiling links dysregulation of MYC expression to the effect of both PKC inhibitor-based drug combinations. Furthermore, MYC overexpression appears as a resistance mechanism to MEK1/2 and KRASG12C inhibitors. Our study provides a rational framework for selecting drugs entering combinatorial strategies and unveils MEK1/2- and KRASG12C-based therapies for lung cancer.

Combination Organelle Mitochondrial Endoplasmic Reticulum Therapy (COMET) for Multidrug Resistant Breast Cancer

It is time for the story of mitochondria and intracellular communication in multidrug resistant cancer to be rewritten. Herein we characterize the extent and cellular advantages of mitochondrial network fusion in multidrug resistant (MDR) breast cancer and have designed a novel nanomedicine that disrupts mitochondrial network fusion and systematically manipulates organelle fusion and function. Combination Organelle Mitochondrial Endoplasmic reticulum Therapy (COMET) is an innovative translational nanomedicine for treating MDR triple negative breast cancer (TNBC) that has superior safety and equivalent efficacy to the current standard of care (paclitaxel). Our study has demonstrated that the increased mitochondrial networks in MDR TNBC contribute to apoptotic resistance and network fusion is mediated by mitofusin2 (MFN2) on the outer mitochondrial membrane. COMET consists of three components; Mitochondrial Network Disrupting (MiND) nanoparticles (NPs) that are loaded with an anti-MFN2 peptide, tunicamycin, and Bam7. The therapeutic rationale of COMET is to reduce the apoptotic threshold in MDR cells with MiND NPs, followed by inducing the endoplasmic reticulum mediated unfolded protein response (UPR) by stressing MDR cells with tunicamycin, and finally, directly inducing mitochondrial apoptosis with Bam7 which is a specific bcl-2 Bax activator. MiND NPs are PEGylated liposomes with the 21 amino acid (2577.98 MW) anti-MFN2 peptide compartmentalized in the aqueous core. Hypoxia (0.5% oxygen) was used to create MDR derivatives of MDA-MB-231 cells and BT-549 cells. Mitochondrial networks were quantified using 3D analysis of 60× live cell images acquired with a Keyence BZ-X710 microscope and MiND NPs effectively fragmented mitochondrial networks in drug sensitive and MDR TNBC cells. The IC50 values, combination index, and dose reduction index derived from dose response studies demonstrate that MiND NPs decrease the apoptotic threshold of both drug sensitive and MDR TNBC cells and COMET is a synergistic drug combination. Complex V (ATP synthase) extracted from bovine cardiac mitochondria was used to assess the effect of MiND NPs on OXPHOS; both MiND NPs and anti-MFN2 peptide solution significantly decrease the activity of mitochondrial complex V and decrease the capacity of OXPHOS. A BacMam viral vector based fluorescent biosensor was used to quantify the unfolded protein response (UPR) at the level of the endoplasmic reticulum and tunicamycin specifically induces the UPR in drug sensitive and MDR TNBC cells. A caspase 3 colorimetric assay demonstrated that the synergistic triple drug combination of COMET increases the ability of Bam7 to specifically induce apoptosis. Dose limiting toxicity and off target effects are a significant challenge for current chemotherapy regimens including paclitaxel. COMET has significantly lower cytotoxicity than paclitaxel in human embryonic kidney epithelial cells and has the potential to fulfill the clinical need for safer cancer therapeutics. COMET is a promising early stage translational nanomedicine for treating MDR TNBC. Manipulating intracellular communication and organelle fusion is a novel approach to treating MDR cancer. The data from this study has rewritten the story of mitochondria, organelle fusion, and intracellular communication and by targeting this intersection, COMET is an exciting new chapter in cancer therapeutics that could transform the clinical outcome of MDR TNBC.