Opportunities and challenges in the development of experimental drug combinations for cancer

Potentials and future perspectives of multi-target drugs in cancer treatment: the next generation anti-cancer agents

Cancer is a major public health problem worldwide with more than an estimated 19.3 million new cases in 2020. The occurrence rises dramatically with age, and the overall risk accumulation is combined with the tendency for cellular repair mechanisms to be less effective in older individuals. Conventional cancer treatments, such as radiotherapy, surgery, and chemotherapy, have been used for decades to combat cancer. However, the emergence of novel fields of cancer research has led to the exploration of innovative treatment approaches focused on immunotherapy, epigenetic therapy, targeted therapy, multi-omics, and also multi-target therapy. The hypothesis was based on that drugs designed to act against individual targets cannot usually battle multigenic diseases like cancer. Multi-target therapies, either in combination or sequential order, have been recommended to combat acquired and intrinsic resistance to anti-cancer treatments. Several studies focused on multi-targeting treatments due to their advantages include; overcoming clonal heterogeneity, lower risk of multi-drug resistance (MDR), decreased drug toxicity, and thereby lower side effects. In this study, we'll discuss about multi-target drugs, their benefits in improving cancer treatments, and recent advances in the field of multi-targeted drugs. Also, we will study the research that performed clinical trials using multi-target therapeutic agents for cancer treatment.

MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction

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

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.

Synthesis, characterization and discovery of multiple anticancer mechanisms of dibutyltin complexes based on salen-like ligands

A series of novel dibutyltin complexes based on salen-like ligands (S01-S03) were synthesized and characterized using ultraviolet-visible spectra，infrared spectra, 1H, 13C, and 119Sn nuclear magnetic resonance, high-resolution mass spectrometry, X-ray crystallography, and thermogravimetric analysis. Complex S03 had excellent anticancer activity in vitro (IC50 = 1.5 ± 0.2 μM in CAL-27 cell lines), which highly activated ROS expression levels and induced apoptosis and cell cycle arrest at the G2/M phase. Interestingly, complex S03 induced cancer cell death through multiple mechanisms (mitochondrial pathway, ER-stress pathway, and DNA damage pathway). This study reveals new mechanisms of organotin complexes and provides new insights into the development of organotin metal complexes as anticancer drugs in the future, and compounds with multiple anticancer mechanisms may be a new strategy for delaying or overcoming drug resistance to chemotherapy and target therapy.

Investigation of the cytotoxicity induced by cannabinoids on human ovarian carcinoma cells

Cannabinoids have been shown to induce anti-tumor activity in a variety of carcinoma cells such as breast, prostate, and brain. The aim of the present study is to investigate the anti-tumor activity of cannabinoids, CBD (cannbidiol), and CBG (cannabigerol) in ovarian carcinoma cells sensitive and resistant to chemotherapeutic drugs. Sensitive A2780 cells and resistant A2780/CP70 carcinoma cells and non-carcinoma cells were exposed to varying concentrations of CBD, CBG, carboplatin or CB1 and CB2 receptor antagonists, AM251 and AM630, respectively, alone or in combination, at different exposure times and cytotoxicity was measured by MTT assay. The mechanism of action of CBD and CB in inducing cytotoxicity was investigated involving a variety of apoptotic and cell cycle assays. Treatment with CBD and CBG selectively, dose and time dependently reduced cell viability and induced apoptosis. The effect of CBD was stronger than CBG in all cell lines tested. Both CBD and CBG induced stronger cytotoxicity than afforded by carboplatin in resistant cells. The cytotoxicity induced by CBD was not CB1 or CB2 receptor dependent in both carcinoma cells, however, CBG-induced cytotoxicity may involve CB1 receptor activity in cisplatin-resistant carcinoma cells. A synergistic effect was observed when cannabinoids at sublethal doses were combined with carboplatin in both carcinoma cells. The apoptotic event may involve loss of mitochondrial membrane potential, Annexin V, caspase 3/7, ROS activities, and cell cycle arrest. Further studies are required to investigate whether these results are translatable in the clinic. Combination therapies with conventional cancer treatments using cannabinoids are suggested.

Combined oncolytic virotherapy gold nanoparticles as synergistic immunotherapy agent in breast cancer control

Combining viruses and nanoparticles may be a way to successfully treat cancer and minimize adverse effects. The current work aimed to evaluate the efficacy of a specific combination of gold nanoparticles (GNPs) and Newcastle disease virus (NDV) to enhance the antitumor effect of breast cancer in both in vitro and in vivo models. Two human breast cancer cell lines (MCF-7 and AMJ-13) and a normal epithelial cell line (HBL-100) were used and treated with NDV and/or GNPs. The MTT assay was used to study the anticancer potentials of NDV and GNP. The colony formation assay and apoptosis markers were used to confirm the killing mechanisms of NDV and GNP against breast cancer cell lines. p53 and caspase-9 expression tested by the qRT-PCR technique. Our results showed that combination therapy had a significant killing effect against breast cancer cells. The findings demonstrated that NDV and GNPs induced apoptosis in cancer cells by activating caspase-9, the p53 protein, and other proteins related to apoptosis, which holds promise as a combination therapy for breast cancer.

Review and Assessment of Policy Options for Improving Access to Combination Therapies in Oncology in Europe

OBJECTIVES

Combinations of on-patent therapies (CTs) are increasingly common in oncology. They cause challenges for funding and affordability, and hence patient access, especially when constituent therapies are owned by different manufacturers. The aim of our study was to develop policy proposals for the assessment, pricing, and funding of CTs and identify which might be relevant in different European countries.

METHODS

Following a review of available literature, seven hypothetical policy proposals were developed and subsequently assessed through 19 semi-structured interviews with health policy, pricing, technology assessment and legal experts in seven European countries to identify those most likely to gain traction.

RESULTS

Experts saw a need for agreed approaches within a country to manage affordability and funding challenges for CTs. Changes to health technology assessment (HTA) and funding models were considered unlikely, but other policy proposals were seen as mostly useful, with country-specific adaptations. Bilateral discussions between manufacturers and payers were deemed important, and less challenging and protracted than arbitrated dialogue between manufacturers. Usage-specific pricing, possibly using weighted average prices, was considered a prerequisite for the financial management of CTs.

CONCLUSIONS

There is a growing need to ensure that CTs are affordable to health systems. It would appear that there is no one set of policies that is appropriate for all countries in Europe, so countries wishing to ensure that patients have (or continue to have) access to CTs of value to them must explore and implement the policies that are best suited to their general approach to funding health care and to the assessment and reimbursement of medicines.

Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature

BACKGROUND

Drug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abstracts faces the challenges of relatively small positive DDI samples among overwhelmingly large negative samples. Random negative sampling and positive sampling are purposely designed to improve the efficiency of AL analysis. The consistency of random negative sampling and positive sampling is shown in the paper.

RESULTS

PubMed abstracts are divided into two pools. Screened pool contains all abstracts that pass the DDI keywords query in PubMed, while unscreened pool includes all the other abstracts. At a prespecified recall rate of 0.95, DDI IR analysis precision is evaluated and compared. In screened pool IR analysis using supporting vector machine (SVM), similarity sampling plus uncertainty sampling improves the precision over uncertainty sampling, from 0.89 to 0.92 respectively. In the unscreened pool IR analysis, the integrated random negative sampling, positive sampling, and similarity sampling improve the precision over uncertainty sampling along, from 0.72 to 0.81 respectively. When we change the SVM to a deep learning method, all sampling schemes consistently improve DDI AL analysis in both screened pool and unscreened pool. Deep learning has significant improvement of precision over SVM, 0.96 vs. 0.92 in screened pool, and 0.90 vs. 0.81 in the unscreened pool, respectively.

CONCLUSIONS

By integrating various sampling schemes and deep learning algorithms into AL, the DDI IR analysis from literature is significantly improved. The random negative sampling and positive sampling are highly effective methods in improving AL analysis where the positive and negative samples are extremely imbalanced.

KGANSynergy: knowledge graph attention network for drug synergy prediction

Combination therapy is widely used to treat complex diseases, particularly in patients who respond poorly to monotherapy. For example, compared with the use of a single drug, drug combinations can reduce drug resistance and improve the efficacy of cancer treatment. Thus, it is vital for researchers and society to help develop effective combination therapies through clinical trials. However, high-throughput synergistic drug combination screening remains challenging and expensive in the large combinational space, where an array of compounds are used. To solve this problem, various computational approaches have been proposed to effectively identify drug combinations by utilizing drug-related biomedical information. In this study, considering the implications of various types of neighbor information of drug entities, we propose a novel end-to-end Knowledge Graph Attention Network to predict drug synergy (KGANSynergy), which utilizes neighbor information of known drugs/cell lines effectively. KGANSynergy uses knowledge graph (KG) hierarchical propagation to find multi-source neighbor nodes for drugs and cell lines. The knowledge graph attention network is designed to distinguish the importance of neighbors in a KG through a multi-attention mechanism and then aggregate the entity's neighbor node information to enrich the entity. Finally, the learned drug and cell line embeddings can be utilized to predict the synergy of drug combinations. Experiments demonstrated that our method outperformed several other competing methods, indicating that our method is effective in identifying drug combinations.

The Effect of Biomarkers on Clinical Trial Risk in Gastric Cancer

OBJECTIVES

This study examined clinical trial success rates for new drug developments in gastric cancer since 1998. We also examined the clinical trial design features that may mitigate the risk of clinical trial failure.

MATERIALS AND METHODS

Clinical trial data was obtained from clinicaltrials.gov. Drugs were included if they entered testing between January 1, 1998 and January 1, 2022 and were excluded if they did not have a completed phase I trial or treated secondary effects of gastric cancer. Transition probabilities were calculated for each phase and compared with industry averages. Success rates were determined based on biomarker usage, drug target, type of therapy, and drug chemistry.

RESULTS

Upon screening 1990 trials, 219 drugs met our inclusion criteria. The probability of a drug completing all phases of testing and obtaining FDA approval was 7%, which is below the 11% industry average. The use of biomarkers in clinical development resulted in nearly a 2-fold increase in the cumulative success rate. Biologics also exhibited higher success rates (17%) as opposed to small molecules (1%). This was true even when we compared both drug types that shared the same target. When comparing only receptor-targeted therapies, biologics (62%) continued to outperform small molecules (18%). Similarly, when narrowed down to drugs targeting solely HER2 receptors, biologics continued to prevail (64% vs. 24%).

CONCLUSIONS

Implementing biomarkers, receptor-targeted therapies, and biologics in clinical development improves clinical trial success rates in gastric cancer. Thus, physicians should prioritize the enrollment of gastric cancer patients in clinical trials that incorporate the aforementioned features.

Targeting Artemisinin-Resistant Malaria by Repurposing the Anti-Hepatitis C Virus Drug Alisporivir

The emergence of Plasmodium falciparum resistance raises an urgent need to find new antimalarial drugs. Here, we report the rational repurposing of the anti-hepatitis C virus drug, alisporivir, a nonimmunosuppressive analog of cyclosporin A, against artemisinin-resistant strains of P. falciparum. In silico docking studies and molecular dynamic simulation predicted strong interaction of alisporivir with PfCyclophilin 19B, confirmed through biophysical assays with a K_d value of 354.3 nM. Alisporivir showed potent antimalarial activity against chloroquine-resistant (PfRKL-9 with resistance index [Ri] 2.14 ± 0.23) and artemisinin-resistant (PfKelch13^R539T with Ri 1.15 ± 0.04) parasites. The Ri is defined as the ratio between the IC₅₀ values of the resistant line to that of the sensitive line. To further investigate the mechanism involved, we analyzed the expression level of PfCyclophilin 19B in artemisinin-resistant P. falciparum (PfKelch13^R539T). Semiquantitative real-time transcript, Western blot, and immunofluorescence analyses confirmed the overexpression of PfCyclophilin 19B in PfKelch13^R539T. A 50% inhibitory concentration in the nanomolar range, together with the targeting of PfCyclophilin 19B, suggests that alisporivir can be used in combination with artemisinin. Since artemisinin resistance slows the clearance of ring-stage parasites, we performed a ring survival assay on artemisinin-resistant strain PfKelch13^R539T and found significant decrease in parasite survival with alisporivir. Alisporivir was found to act synergistically with dihydroartemisinin and increase its efficacy. Furthermore, alisporivir exhibited antimalarial activity in vivo. Altogether, with the rational target-based Repurposing of alisporivir against malaria, our results support the hypothesis that targeting resistance mechanisms is a viable approach toward dealing with drug-resistant parasite.

Purinergic Receptors P2X7 and P2X4 as Markers of Disease Progression in the rd10 Mouse Model of Inherited Retinal Dystrophy

The purinergic receptor P2X7 (P2X7R) is implicated in all neurodegenerative diseases of the central nervous system. It is also involved in the retinal degeneration associated with glaucoma, age-related macular degeneration, and diabetic retinopathy, and its overexpression in the retina is evident in these disorders. Retinitis pigmentosa is a progressive degenerative disease that ultimately leads to blindness. Here, we investigated the expression of P2X7R during disease progression in the rd10 mouse model of RP. As the purinergic receptor P2X4 is widely co-expressed with P2X7R, we also studied its expression in the retina of rd10 mice. The expression of P2X7R and P2X4R was examined by immunohistochemistry, flow cytometry, and western blotting. In addition, we analyzed retinal functionality by electroretinographic recordings of visual responses and optomotor tests and retinal morphology. We found that the expression of P2X7R and P2X4R increased in rd10 mice concomitant with disease progression, but with different cellular localization. Our findings suggest that P2X7R and P2X4R might play an important role in RP progression, which should be further analyzed for the pharmacological treatment of inherited retinal dystrophies.

NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction

Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug–cancer cell line features, but there is still a need to explore methods to combine topological information in the protein interaction network (PPI). Therefore, we propose a network-embedding-based prediction model, NEXGB, which integrates the corresponding protein modules of drug–cancer cell lines with PPI network information. NEXGB extracts the topological features of each protein node in a PPI network by struc2vec. Then, we combine the topological features with the target protein information of drug–cancer cell lines, to generate drug features and cancer cell line features, and utilize extreme gradient boosting (XGBoost) to predict the synergistic relationship between drug combinations and cancer cell lines. We apply our model on two recently developed datasets, the Oncology-Screen dataset (Oncology-Screen) and the large drug combination dataset (DrugCombDB). The experimental results show that NEXGB outperforms five current methods, and it effectively improves the predictive power in discovering relationships between drug combinations and cancer cell lines. This further demonstrates that the network information is valid for detecting combination therapies for cancer and other complex diseases.

Why We Need a New Outcomes-Based Value Attribution Framework for Combination Regimens in Oncology

BACKGROUND

Novel oncology treatment strategies increasingly use medicines with distinct but complementary mechanisms of action in combination or in close sequence. Payers, when confronted with higher total cost of providing combination regimens involving multiple therapies and usually longer treatment durations, are reluctant to reimburse them, particularly when they perceive the expected incremental benefits from adding a new medicine (the add-on) to a currently reimbursed medicine (the backbone) not to represent value for money to the health system. Nevertheless, depending on how value is attributed to the add-on versus the backbone, a clinically effective medicine used as part of a regimen that increases treatment duration might be found "not cost-effective at zero price." This phenomenon, signaling a policy problem not a pricing issue, first needs to be better understood before a generalizable and transparent solution can be presented.

OBJECTIVE

This article sets out when this policy challenge arises and describes general principles that any proposed solution to the value attribution problem must satisfy.

METHODS

We develop a simplified conceptual framework and use this to address 2 topics. The first is to understand the origin of problems posed by the current approach for attributing value in incremental cost-effectiveness analyses of combination regimens. The second is to discuss 2 new approaches in the literature designed to address the challenge.

FINDINGS

We find that neither meets our criteria, meaning that further work is needed to resolve the issue. Finally, we briefly discuss the implications of relaxing the simplifying assumptions in our conceptual framework.

Palbociclib in combination with sunitinib exerts a synergistic anti-cancer effect in patient-derived xenograft models of various human cancers types

The efficacy/safety of combining palbociclib (a CDK4/6 inhibitor) and sunitinib (a multi-targeted receptor tyrosine kinase inhibitor) was evaluated, using patient-derived xenograft (PDX) models. Twenty-three PDX mice models were developed from patients with various solid tumors. The mice were randomized to 4 groups (5-6 mice in each): control/palbociclib (100 mg/kg)/sunitinib (50 mg/kg)/combination. Drugs were administered orally, 5 days/week. In 17/23 PDX models (74%), the combination demonstrated a synergistic inhibitory effect vs the monotherapies ("responder" models) with no unexpected toxicities. In 13/17 responder models, where standard-of-care (SOC) was an additional comparator, the combination was more effective than SOC in 7 models, as effective in 4, and less effective in 2. The mean ± SEM experiment duration in 15/17 responder models (2/17 were excluded due to technical issues) was 86 ± 12 and 31 ± 5 days for the combination and control groups, respectively (p = 0.0002). The effect of the combination was dose-dependent. Cell-viability experiments in A549/MDA-MB-231/HT-29 cell lines and experiments using tumor-derived primary cell spheroids supported the PDX findings. In conclusion, combination of palbociclib and sunitinib exerts a synergistic anti-tumor effect without adding unexpected toxicity. A clinical trial assessing this combination is underway.

Recent Advances in Dual BRD4-Kinase Inhibitors Base on Polypharmacology

Epigenetic reader BRD4 is involved in chromatin remodeling and transcriptional regulation, making it a promising therapeutic target. However, during the past decades, the results of many BRD4 inhibitors that have entered clinical trials were, in the main, unsatisfactory, due to some therapeutic limitations such as off-target effects and drug resistance. Combining a BRD4 inhibitor with another drug was expected to be an ideal option to overcome these "bottlenecks" and achieve improved therapeutic outcomes. However, combination therapy might trigger toxicity caused by drug-drug interaction, complex pharmacokinetic and additive effects. Recently, the application of dual-target drugs targeting BRD4 and other kinases has emerged to be an attractive approach to remedy defects of a single BRD4 inhibitor. Herein, this review focuses on recent advances in the discovery of dual BRD4-kinase inhibitors, with emphasis on their co-crystal structures and structure-activity relationships (SARs), as well as perspective prospects in the field.

SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning

Drug combination therapy has become a promising therapeutic strategy for cancer treatment. While high-throughput drug combination screening is effective for identifying synergistic drug combinations, measuring all possible combinations is impractical due to the vast space of therapeutic agents and cell lines. In this study, we propose a biologically-motivated deep learning approach to identify pathway-level features from drug and cell lines' molecular data for predicting drug synergy and quantifying the interactions in synergistic drug pairs. This method obtained an MSE of 70.6{plus minus}6.4, significantly surpassing previous approaches while providing potential candidate pathways to explain the prediction. We further demonstrate that drug combinations tend to be more synergistic when their top contributing pathways are closer to each other on a protein interaction network, suggesting a potential strategy for combination therapy with topologically interacting pathways. Our computational approach can thus be utilized both for pre-screening of potential drug combinations and for designing new combinations based on proximity of pathways associated with drug targets and cell lines. Implications: Our computational framework may be translated in the future to clinical scenarios where synergistic drugs are tailored to the patient and additionally, drug development could benefit from designing drugs that target topologically close pathways.

Non-parametric synergy modeling of chemical compounds with Gaussian processes

Background

Understanding the synergetic and antagonistic effects of combinations of drugs and toxins is vital for many applications, including treatment of multifactorial diseases and ecotoxicological monitoring. Synergy is usually assessed by comparing the response of drug combinations to a predicted non-interactive response from reference (null) models. Possible choices of null models are Loewe additivity, Bliss independence and the recently rediscovered Hand model. A different approach is taken by the MuSyC model, which directly fits a generalization of the Hill model to the data. All of these models, however, fit the dose–response relationship with a parametric model.

Results

We propose the Hand-GP model, a non-parametric model based on the combination of the Hand model with Gaussian processes. We introduce a new logarithmic squared exponential kernel for the Gaussian process which captures the logarithmic dependence of response on dose. From the monotherapeutic response and the Hand principle, we construct a null reference response and synergy is assessed from the difference between this null reference and the Gaussian process fitted response. Statistical significance of the difference is assessed from the confidence intervals of the Gaussian process fits. We evaluate performance of our model on a simulated data set from Greco, two simulated data sets of our own design and two benchmark data sets from Chou and Talalay. We compare the Hand-GP model to standard synergy models and show that our model performs better on these data sets. We also compare our model to the MuSyC model as an example of a recent method on these five data sets and on two-drug combination screens: Mott et al. anti-malarial screen and O’Neil et al. anti-cancer screen. We identify cases in which the HandGP model is preferred and cases in which the MuSyC model is preferred.

Conclusion

The Hand-GP model is a flexible model to capture synergy. Its non-parametric and probabilistic nature allows it to model a wide variety of response patterns.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04508-7.

Prediction of synergistic drug combinations using PCA-initialized deep learning

Background

Cancer is one of the main causes of death worldwide. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. However, the immense number of possible drug combinations and large synergistic space makes it infeasible to screen all effective drug pairs experimentally. Therefore, it is crucial to develop computational approaches to predict drug synergy and guide experimental design for the discovery of rational combinations for therapy.

Results

We present a new deep learning approach to predict synergistic drug combinations by integrating gene expression profiles from cell lines and chemical structure data. Specifically, we use principal component analysis (PCA) to reduce the dimensionality of the chemical descriptor data and gene expression data. We then propagate the low-dimensional data through a neural network to predict drug synergy values. We apply our method to O’Neil’s high-throughput drug combination screening data as well as a dataset from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. We compare the neural network approach with and without dimension reduction. Additionally, we demonstrate the effectiveness of our deep learning approach and compare its performance with three state-of-the-art machine learning methods: Random Forests, XGBoost, and elastic net, with and without PCA-based dimensionality reduction.

Conclusions

Our developed approach outperforms other machine learning methods, and the use of dimension reduction dramatically decreases the computation time without sacrificing accuracy.

The future of antibiotics begins with discovering new combinations

Antibiotic resistance is a worldwide and growing clinical problem. With limited drug development in the antibacterial space, combination therapy has emerged as a promising strategy to combat multidrug‐resistant bacteria. Antibacterial combinations can improve antibiotic efficacy and suppress antibacterial resistance through independent, synergistic, or even antagonistic activities. Combination therapies are famously used to treat viral and mycobacterial infections and cancer. However, antibacterial combinations are only now emerging as a common treatment strategy for other bacterial infections owing to challenges in their discovery, development, regulatory approval, and commercial/clinical deployment. Here, we focus on discovery—where the sheer scale of combinatorial chemical spaces represents a significant challenge—and discuss how combination therapy can impact the treatment of bacterial infections. Despite these challenges, recent advancements, including new in silico methods, theoretical frameworks, and microfluidic platforms, are poised to identify the new and efficacious antibacterial combinations needed to revitalize the antibacterial drug pipeline.

Challenges in valuing and paying for combination regimens in oncology: reporting the perspectives of a multi-stakeholder, international workshop

BACKGROUND

It is increasingly common for two or more treatments for cancer to be combined as a single regimen. Determining value and appropriate payment for such regimens can be challenging. This study discusses these challenges, and possible solutions.

METHODS

Stakeholders from around the world attended a 2-day workshop, supported by a background paper. This study captures key outcomes from the discussion, but is not a consensus statement.

RESULTS

Workshop attendees agreed that combining on-patent treatments can result in affordability and value for money challenges that delay or deny patient access to clinically effective treatments in many health systems. Options for addressing these challenges include: (i) Increasing the value of combination therapies through improved clinical development; (ii) Willingness to pay more for combinations than for single drugs offering similar benefit, or; (iii) Aligning the cost of constituent therapies with their value within a regimen. Workshop attendees felt that (i) and (iii) merited further discussion, whereas (ii) was unlikely to be justifiable. Views differed on the feasibility of (i). Key to (iii) would be systems allowing different prices to apply to different uses of a drug.

CONCLUSIONS

Common ground was identified on immediate actions to improve access to combination regimens. These include an exploration of the legal challenges associated with price negotiations, and ensuring that pricing systems can support implementation of negotiated prices for specific uses. Improvements to clinical development and trial design should be pursued in the medium and longer term.

Exploration of 7-azaindole-coumaranone hybrids and their analogues as protein kinase inhibitors

7-Azaindole has been labelled a privileged scaffold for the design of new potent inhibitors of protein kinases. In this paper, we determined the inhibition profiles of novel mono- and disubstituted derivatives of 7-azaindole-coumaranone hybrids on various disease-related protein kinases. Eight hit compounds were identified, including a potent Haspin inhibitor with an IC₅₀ value of 0.15 μM. An interesting observation was that all active monosubstituted compounds displayed dual inhibition for Haspin and GSK-3β, while disubstituted derivatives inhibited GSK-3β and LmCK1 from Leishmania major parasite. Analyses of structure activity relationships (SARs) also revealed that mono-substitution with para-fluorobenzyloxy ring produced an equipotent inhibition of Haspin and GSK-3β. Haspin and GSK-3β are relevant targets for developing new anticancer agents while LmCK1 is an innovative target for leishmanicidal drugs. Novel compounds reported in this paper constitute promising starting points for the development of new anticancer and leishmanicidal drugs.

Control Strategies for Carcinogenic-Associated Helminthiases: An Integrated Overview

Helminthiases are extremely prevalent in the developing world. In addition, the chronic infection with some parasitic worms are classified as carcinogenic. Therefore, it is utmost importance to understand the parasite-host interactions, the mechanisms underlay carcinogenesis and how they could be counteracted. This knowledge may ultimately guide novel control strategies that include chemotherapy-based approaches targeting these pathogens and associated pathologies caused by their infections. Little is known on how some helminthiases are associated with cancer; however, it has been hypothesized that chemical carcinogenesis may be involved in the process. Here, we summarize the current knowledge on chemical carcinogenesis associated with helminthiases, along with available therapeutic options and potential therapeutic alternatives including chemotherapy and/or immunotherapy. Ideally, the treatment of the carcinogenic helminthiases should target both the parasite and associated pathologies. The success of any chemotherapeutic regimen often depends on the host immune response during the infection and nutritional status among other factors. The close association between chemotherapy and cell-mediated immunity suggests that a dual therapeutic approach would be advantageous. In addition, there is a pressing need for complementary drugs that antagonize the carcinogenesis process associated with the helminth infections.

Cell detachment ratio on pH-responsive chitosan: A useful biometric for prognostic judgment and drug efficacy assessment in oncology

The inherently unpredictable complexity of tumors impedes the widespread practice of the molecular biomarkers in outcome prediction. Alternatively, from the biophysical perspective, this study sought to investigate the applicability of the cell detachment ratio (CDR) derived from pH-responsive chitosan as a biometrical identifier for the disease state in cancer prognostic judgment and drug efficacy assessment. In the targeted therapy model, the repression of tumor dissemination in cells harboring aberrant ErbB signals (human non-small cell lung cancer cell line PC9 and breast cancer cell line BT474) were first demonstrated both in vitro and in vivo. Consequently, the corresponding CDR profile goes synchronously with the extent of cancer regression in response to the medication. Definitive integrins that drive the cell detachment were also verified through CDR examination following the integrin functional blockade. Conclusively, CDR is a promising clinical index for evaluation of the metastatic cell behaviors in terms of the cell detachment.

Moonshots for aging

 As the global population ages, there is increased interest in living longer and improving one’s quality of life in later years. However, studying aging – the decline in body function – is expensive and time-consuming. And despite research success to make model organisms live longer, there still aren’t really any feasible solutions for delaying aging in humans. With space travel, scientists and engineers couldn’t know what it would take to get to the moon. They had to extrapolate from theory and shorter-range tests. Perhaps with aging, we need a similar moonshot philosophy. And though “shot” might imply medicine, perhaps we need to think beyond medical interventions. Like the moon once was, we seem a long way away from provable therapies to increase human healthspan (the healthy period of one’s life) or lifespan (how long one lives). This review therefore focuses on radical proposals. We hope it might stimulate discussion on what we might consider doing significantly differently than ongoing aging research.

Synergistic anticancer effects of curcumin and 3',4'-didemethylnobiletin in combination on colon cancer cells

Chemoprevention strategies employing the use of multiple dietary bioactive components and their metabolites in combination offer advantages due to their low toxicity and potential synergistic interactions. Herein, for the first time, we studied the combination of curcumin and 3',4'-didemethylnobiletin (DDMN), a primary metabolite of nobiletin, to determine their combinatory effects in inhibiting growth of human colon cancer cells. Isobologram analysis revealed a synergistic interaction between curcumin and DDMN in the inhibition of cell growth of HCT116 colon cancer cells. The combination treatment induced significant G₂ -M cell-cycle arrest and extensive apoptosis, which greatly exceeded the effects of individual treatments with curcumin or DDMN. Proteins associated with these heightened anticarcinogenic effects were p53, p21, HO-1, c-poly(ADP-ribose) polymerase, Cdc2, and Cdc25c; each of the proteins was confirmed to be substantially impacted by the combination treatment, more than by individual treatments alone. Interestingly, an increase in the stability of curcumin was also observed with the presence of DDMN in cell culture medium, which could offer an explanation in part for the synergistic interaction between curcumin and DDMN. This newly identified synergy between curcumin and DDMN should be explored further to determine its chemopreventive potential against colon cancer in vivo. PRACTICAL APPLICATION: This study identifies for the first time the synergistic inhibition of colon cancer cell growth by the dietary component curcumin present in turmeric, in combination with a metabolite of nobiletin, a unique citrus flavonoid. The synergism of the combination may be due to cell-cycle arrest and apoptosis induced by the combination as well as an improvement in the stability of curcumin as a result of the antioxidant property of the nobiletin metabolite. These significant findings of synergism between curcumin and the nobiletin metabolite could offer potential chemopreventive value against colon cancer.

Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization

Drug combinations may reduce toxicity and increase therapeutic efficacy, offering a promising strategy to conquer multiple complex diseases. However, due to large-scale combinatorial space, it remains challenging to identify effective combinations. Although many computational methods have focused on predicting drug synergy to reduce combinatorial space, they fail to effectively consider multiple sources of important knowledge. Thus, it is necessary to propose a computational method that can exploit useful information to predict drug synergy. Here, we developed a computational method to predict drug synergy based on graph co-regularization, named DSGCR. By incorporating drug-target network patterns, pharmacological patterns, and prior knowledge of drug combinations, DSGCR performs predictions of synergistic drug combinations. Compared to several existing methods, DSGCR achieves superior performance in predicting drug synergy in terms of various metrics via cross-validation. Additionally, we analyzed the importance of various sources of drug knowledge concerning three DSGCR's scenarios. Finally, the potential of DSGCR to score drug synergy was confirmed by three predicted synergistic drug combinations.

A Comprehensive Review on MAPK: A Promising Therapeutic Target in Cancer

The mitogen-activated protein kinase (MAPK) pathway is an important bridge in the switch from extracellular signals to intracellular responses. Alterations of signaling cascades are found in various diseases, including cancer, as a result of genetic and epigenetic changes. Numerous studies focused on both the homeostatic and the pathologic conduct of MAPK signaling; however, there is still much to be deciphered in terms of regulation and action models in both preclinical and clinical research. MAPK has implications in the response to cancer therapy, particularly the activation of the compensatory pathways in response to experimental MAPK inhibition. The present paper discusses new insights into MAPK as a complex cell signaling pathway with roles in the sustenance of cellular normal conduit, response to cancer therapy, and activation of compensatory pathways. Unfortunately, most MAPK inhibitors trigger resistance due to the activation of compensatory feed-back loops in tumor cells and tumor microenvironment components. Therefore, novel combinatorial therapies have to be implemented for cancer management in order to restrict the possibility of alternative pathway activation, as a perspective for developing novel therapies based on integration in translational studies.

Anti-Cancer Effects of Synergistic Drug-Bacterium Combinations on Induced Breast Cancer in BALB/c Mice

Cancer development and progression are extremely complex due to the alteration of various genes and pathways. In most cases, multiple agents are required to control cancer progression. The purpose of this study is to investigate, using a mouse model, the synergistic interactions of anti-cancer agents, 1'-S-1'-acetoxychavicol acetate (ACA), Mycobacterium indicus pranii (MIP), and cisplatin (CDDP) in double and triple combinations to treat chemo-sensitize and immune-sensitize breast cancer. Changes in tumor volume and body weight were monitored. Organs were harvested and stained using hematoxylin-eosin for histopathological assessment. Milliplex enzyme-linked immunosorbent assay (ELISA) was performed to determine cytokine levels, while immunohistochemistry (IHC) was conducted on tumor biopsies to verify systemic drug effects. In vivo mouse models showed tumor regression with maintenance of regular body weight for all the different treatment regimens. IHC results provided conclusive evidence indicating that combination regimens were able to down-regulate nuclear factor kappa-B activation and reduce the expression of its regulated pro-inflammatory proteins. Reduction of pro-inflammatory cytokines (e.g., IL-6, TNF-α, and IFN-ɣ) levels were observed when using the triple combination, which indicated that the synergistic drug combination was able to significantly control cancer progression. In conclusion, ACA, MIP, and CDDP together serve as promising candidates for further development and for subsequent clinical trials against estrogen-sensitive breast cancer.

Design and synthesis of pentacyclic triterpene conjugates and their use in medicinal research

Triterpenoids are natural products from plants and many other organisms that have various biological activities, such as antitumor, antiviral, antimicrobial, and protective activities. This review covers the synthesis and biological evaluation of pentacyclic triterpene (PT) conjugates with other molecules that have been found to increase the IC₅₀ or improve the pharmacological profile of the parent PT. Some of these molecules are designed to target specific proteins or cellular organelles, which has resulted in highly selective lead structures for drug development. Other PT conjugates are useful for investigating their mechanism of action. This concept has been very successful: 1) Many compounds, especially mitochondria-targeting PT conjugates, have reached a selective cytotoxicity at low nanomolar concentrations in cancer cells. 2) A number of PT conjugates have had high activity against HIV or the influenza virus. 3) Fluorescent PT conjugates have been able to visualize the PT in living cells, which has allowed quantification of the uptake and distribution of the PT within the cell. 4) Biotinylated PT conjugates have been used to identify target proteins, which may help to show their mechanism of action. 5) A large number of PT conjugates with polyethylene glycol (PEG), polyamines, etc. form nanometer-sized micelles that have a much better pharmacological profile than the PT alone. In summary, the connection of a PT to an appropriate modifying molecule has resulted in extremely useful semisynthetic compounds with a high potential to treat cancer or viral infections or compounds that are useful for the study of the mechanism of action of PTs at the molecular level.

The antimalarial drug mefloquine enhances TP53 premature termination codon readthrough by aminoglycoside G418

Nonsense mutations constitute ~10% of TP53 mutations in cancer. They introduce a premature termination codon that gives rise to truncated p53 protein with impaired function. The aminoglycoside G418 can induce TP53 premature termination codon readthrough and thus increase cellular levels of full-length protein. Small molecule phthalimide derivatives that can enhance the readthrough activity of G418 have also been described. To determine whether readthrough enhancers exist among drugs that are already approved for use in humans, we tested seven antimalarial drugs for readthrough of the common R213X TP53 nonsense mutation in HDQ-P1 breast cancer cells. Mefloquine induced no TP53 readthrough activity as a single agent but it strongly potentiated readthrough by G418. The two enantiomers composing pharmaceutical mefloquine potentiated readthrough to similar levels in HDQ-P1 cells and also in SW900, NCI-H1688 and HCC1937 cancer cells with different TP53 nonsense mutations. Exposure to G418 and mefloquine increased p53 phosphorylation at Ser15 and P21 transcript levels following DNA damage, indicating p53 produced via readthrough was functional. Mefloquine does not appear to enhance readthrough via lysosomotropic effects as it did not significantly affect lysosomal pH, the cellular levels of G418 or its distribution in organellar or cytosolic fractions. The availability of a readthrough enhancer that is already approved for use in humans should facilitate study of the therapeutic potential of TP53 readthrough in preclinical cancer models.

Phytomedicines in the Treatment of Migraine

Migraine is a disabling neurovascular disorder with few targeted, tolerable and effective treatments. Phytomedicines, or plant-based medicinal formulations, hold great promise in the identification of novel therapeutic targets in migraine. Many patients also turn toward herbal and plant-based therapies for the treatment of their migraines as clinical and preclinical evidence of efficacy increases. Patients seek effective and tolerable treatments instead of or in addition to current conventional pharmacologic therapies. We review some phytomedicines potentially useful for migraine treatment-feverfew (Tanacetum parthenium), butterbur (Petasites hybridus), marijuana (Cannabis spp.), Saint John's Wort (Hypericum perforatum) and the Damask rose (Rosa × damascena)-with respect to their mechanisms of action and evidence for treatment of migraine. The evidence for feverfew is mixed; butterbur is effective with potential risks of hepatotoxicity related to preparation; marijuana has not been shown to be effective in migraine treatment, and data are scant; Saint John's Wort shows relevant physiological activity but is a hepatic enzyme inducer and lacks clinical studies for this purpose; the Damask rose when used in topical preparations did not show efficacy in one clinical trial. Other plant preparations have been considered for migraine treatment but most without blinded randomized, placebo-controlled trial evidence.

Genie in a bottle: controlled release helps tame natural polypharmacology?

Ability to faithfully report drug-target interactions constitutes a major critical parameter in preclinical/clinical settings. Yet the assessment of target engagement remains challenging, particularly for promiscuous and/or polypharmacologic ligands. Drawing from our improved insights into native electrophile signaling and emerging technologies that profile and interrogate these non-enzyme-assisted signaling subsystems, we posit that 'trained' polypharmocologic covalent inhibitors can be designed. Accumulating evidence indicates that electrophile-modified states at fractional occupancy can alter cell fate. Thus, by understanding sensing preferences and ligandable regions favored by the natural electrophilic signals at individual protein-ligand resolution, we can better evaluate target engagement and develop a function-guided understanding of polypharmacology.

Designing Dual Inhibitors of Anaplastic Lymphoma Kinase (ALK) and Bromodomain-4 (BRD4) by Tuning Kinase Selectivity

Concomitant inhibition of anaplastic lymphoma kinase (ALK) and bromodomain-4 (BRD4) is a potential therapeutic strategy for targeting two key oncogenic drivers that co-segregate in a significant fraction of high-risk neuroblastoma patients, mutation of ALK and amplification of MYCN. Starting from known dual polo-like kinase (PLK)-1-BRD4 inhibitor BI-2536, we employed structure-based design to redesign this series toward compounds with a dual ALK-BRD4 profile. These efforts led to compound ( R)-2-((2-ethoxy-4-(1-methylpiperidin-4-yl)phenyl)amino)-7-ethyl-5-methyl-8-((4-methylthiophen-2-yl)methyl)-7,8-dihydropteridin-6(5 H)-one (16k) demonstrating improved ALK activity and significantly reduced PLK-1 activity, while maintaining BRD4 activity and overall kinome selectivity. We demonstrate the compounds' on-target engagement with ALK and BRD4 in cells as well as favorable broad kinase and bromodomain selectivity.

How to schedule VEGF and PD-1 inhibitors in combination cancer therapy?

Background

One of the questions in the design of cancer clinical trials with combination of two drugs is in which order to administer the drugs. This is an important question, especially in the case where one agent may interfere with the effectiveness of the other agent.

Results

In the present paper we develop a mathematical model to address this scheduling question in a specific case where one of the drugs is anti-VEGF, which is known to affect the perfusion of other drugs. As a second drug we take anti-PD-1. Both drugs are known to increase the activation of anticancer T cells. Our simulations show that in the case where anti-VEGF reduces the perfusion, a non-overlapping schedule is significantly more effective than a simultaneous injection of the two drugs, and it is somewhat more beneficial to inject anti-PD-1 first.

Conclusion

The method and results of the paper can be extended to other combinations, and they could play an important role in the design of clinical trials with combination therapy, where scheduling strategies may significantly affect the outcome.

Combined antitumoral effects of pretubulysin and methotrexate

Pretubulysin (PT), a potent tubulin-binding antitumoral drug, and the well-established antimetabolite methotrexate (MTX) were tested separately or in combination (PT+MTX) for antitumoral activity in L1210 leukemia cells or KB cervix carcinoma cells in vitro and in vivo in NMRI-nu/nu tumor mouse models. In cultured L1210 cells, treatment with PT or MTX displays strong antitumoral effects in vitro, and the combination PT+MTX exceeds the effect of single drugs. PT also potently kills the MTX resistant KB cell line, without significant MTX combination effect. Cell cycle analysis reveals the expected arrest in G1/S by MTX and in G2/M by PT. In both cell lines, the PT+MTX combination induces a G2/M arrest which is stronger than the PT-triggered G2/M arrest. PT+MTX does not change rates of apoptotic L1210 or KB cells as compared to single drug applications. Confocal laser scanning microscopy images show the microtubule disruption and nuclear fragmentation induced by PT treatment of L1210 and KB cells. MTX changes the architecture of the F-actin skeleton. PT+MTX combines the toxic effects of both drugs. In the in vivo setting, the antitumoral activity of drugs differs from their in vitro cytotoxicity, but their combination effects are more pronounced. MTX on its own does not display significant antitumoral activity, whereas PT reduces tumor growth in both L1210 and KB in vivo models. Consistent with the cell cycle effects, MTX combined at moderate dose boosts the antitumoral effect of PT in both in vivo tumor models. Therefore, the PT+MTX combination may present a promising therapeutic approach for different types of cancer.

Metal-based antitumor compounds: beyond cisplatin

Despite improvements in the 5-year survival rate to over 80% in cancers, such as Hodgkin lymphoma and testicular cancer, more aggressive tumors including pancreatic and brain cancer still have extremely low survival rates. The establishment of chemoresistance, responsible for the reduction in treatment efficiency and cancer relapse, is one possible explanation for this setback. Metal-based compounds, a class of anticancer drugs, are largely used in the treatment of cancer. Herein, we will review the use of metal-based small molecules in chemotherapy, focusing on recent studies, and we will discuss how new nonplatinum-based agents are prompting scientists to increase drug specificity to overcome chemoresistance in cancer cells.

Pairwise combinations of chemical compounds that delay yeast chronological aging through different signaling pathways display synergistic effects on the extent of aging delay

We have recently discovered six plant extracts that delay yeast chronological aging. Most of them affect different nodes, edges and modules of an evolutionarily conserved network of longevity regulation that integrates certain signaling pathways and protein kinases; this network is also under control of such aging-delaying chemical compounds as spermidine and resveratrol. We have previously shown that, if a strain carrying an aging-delaying single-gene mutation affecting a certain node, edge or module of the network is exposed to some of the six plant extracts, the mutation and the plant extract enhance aging-delaying efficiencies of each other so that their combination has a synergistic effect on the extent of aging delay. We therefore hypothesized that a pairwise combination of two aging-delaying plant extracts or a combination of one of these plant extracts and spermidine or resveratrol may have a synergistic effect on the extent of aging delay only if each component of this combination targets a different element of the network. To test our hypothesis, we assessed longevity-extending efficiencies of all possible pairwise combinations of the six plant extracts or of one of them and spermidine or resveratrol in chronologically aging yeast. In support of our hypothesis, we show that only pairwise combinations of naturally-occurring chemical compounds that slow aging through different nodes, edges and modules of the network delay aging in a synergistic manner.

ComPAS: A Bayesian drug combination platform trial design with adaptive shrinkage

Combining different treatment regimens provides an effective approach to induce a synergistic treatment effect and overcome resistance to monotherapy. The challenge is that, given the large number of existing monotherapies, the number of possible combinations is huge and new potentially more efficacious compounds may become available any time during drug development. To address this challenge, we propose a flexible Bayesian drug combination platform design with adaptive shrinkage (ComPAS), which allows for dropping futile combinations, graduating effective combinations, and adding new combinations during the course of the trial. A new adaptive shrinkage method is developed to adaptively borrow information across combinations and efficiently identify the efficacious combinations based on Bayesian model selection and hierarchical models. Simulation studies show that ComPAS identifies the effective combinations with higher probability than some existing designs. ComPAS provides an efficient and flexible platform to accelerate drug development in a seamless and timely fashion.

A Phase I Study of Ganetespib and Ziv-Aflibercept in Patients with Advanced Carcinomas and Sarcomas

LESSONS LEARNED

The combination of the antiangiogenic agent ziv-aflibercept and the heat shock protein 90 inhibitor ganetespib was associated with several serious and unexpected adverse events and was not tolerable on the dosing schedule tested.Studies such as these emphasize the importance of considering overlapping toxicities when designing novel treatment combination regimens.

BACKGROUND

Although inhibition of angiogenesis is an effective strategy for cancer treatment, acquired resistance to antiangiogenic therapy is common. Heat shock protein 90 (Hsp90) is a molecular chaperone that regulates various oncogenic signaling pathways involved in acquired resistance and has been shown to play a role in angiogenesis. Combining an antiangiogenic agent with an Hsp90 inhibitor has therefore been proposed as a strategy for preventing resistance and improving antitumor activity. We conducted a single-arm phase I study evaluating the combination of ziv-aflibercept, an antiangiogenic drug, with the Hsp90 inhibitor ganetespib.

METHODS

Adult patients were eligible if they had recurrent or metastatic gastrointestinal carcinomas, nonsquamous non-small cell lung carcinomas, urothelial carcinomas, or sarcomas that had progressed after at least one line of standard therapy. Ziv-aflibercept was administered intravenously on days 1 and 15, and ganetespib was administered intravenously on days 1, 8, and 15, of each 28-day cycle.

RESULTS

Five patients were treated with the combination. Although three patients achieved stable disease, study treatment was associated with several serious and unexpected adverse events.

CONCLUSION

The dose escalation phase of this study was not completed, but the limited data obtained suggest that this combination may be too toxic when administered on this dosing schedule.

Effects of Two-by-Two Combination Therapy with Valproic Acid, Lithium Chloride, and Celecoxib on the Angiogenesis of the Chicken Chorioallantoic Membrane

BACKGROUND

The synergistic effects of valproic acid (VPA), lithium (Li), and celecoxib (CX) have been shown in combination therapy against the proliferation and metastasis of numerous cancers. Angiogenesis plays a critical role in the pathogenesis of tumor growth and metastasis. The aim of the present study was to evaluate the antiangiogenic effects of VPA, lithium chloride (LiCl), and CX, alone or in 2-by-2 combinations, using the chicken chorioallantoic membrane (CAM) assay.

METHODS

Fertilized chicken eggs were randomly divided into 10 groups: control, VPA (1.8 and 3.6 µmol/CAM), Li (0.15 and 0.60 µmol/CAM), CX (0.02 and 0.08 µmol/CAM), VPA+Li, VPA+CX, and CX+Li (n=10 per group). A window was made on the eggshells and the CAMs were exposed to a filter disk containing VPA, LiCl, and CX, alone or in 2-by-2 combinations. The control CAMs were treated with distilled water (vehicle). Three days after the treatment, the number of vessel branch points was counted in each CAM. The data were analyzed using SPSS, version 15.One-way ANOVA, followed by the Tukey tests, was used to compare the groups. A P<0.05 was considered a statistically significant difference between the groups.

RESULTS

According to the results, all the tested drugs decreased the number of the vessel branch points in a dose-dependent manner compared to the control group (P<0.001). In addition, combinations of the drugs were more effective in decreasing angiogenesis than the use of each drug alone.

CONCLUSION

These findings suggest that 2-by-2 combinations of VPA, CX, and LiCl can be considered an effective antiangiogenesis therapeutic modality.

Network Propagation Predicts Drug Synergy in Cancers

Combination therapies are commonly used to treat patients with complex diseases that respond poorly to single-agent therapies. In vitro high-throughput drug screening is a standard method for preclinical prioritization of synergistic drug combinations, but it can be impractical for large drug sets. Computational methods are thus being actively explored; however, most published methods were built on a limited size of cancer cell lines or drugs, and it remains a challenge to predict synergism at a large scale where the diversity within the data escalates the difficulty of prediction. Here, we present a state-of-the-field synergy prediction algorithm, which ranked first in all subchallenges in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. The model was built and evaluated using the largest drug combination screening dataset at the time of the competition, consisting of approximately 11,500 experimentally tested synergy scores of 118 drugs in 85 cancer cell lines. We developed a novel feature extraction strategy by integrating the cross-cell and cross-drug information with a novel network propagation method and then assembled the information in monotherapy and simulated molecular data to predict drug synergy. This represents a significant conceptual advancement of synergy prediction, using extracted features in the form of simulated posttreatment molecular profiles when only the pretreatment molecular profile is available. Our cross-tissue synergism prediction algorithm achieves promising accuracy comparable with the correlation between experimental replicates and can be applied to other cancer cell lines and drugs to guide therapeutic choices.Significance: This study presents a novel network propagation-based method that predicts anticancer drug synergy to the accuracy of experimental replicates, which establishes a state-of-the-field method as benchmarked by the pharmacogenomics research community involving models generated by 160 teams. Cancer Res; 78(18); 5446-57. ©2018 AACR.

Inactivation of nuclear factor κB by MIP-based drug combinations augments cell death of breast cancer cells

Background

Drug combination therapy to treat cancer is a strategic approach to increase successful treatment rate. Optimizing combination regimens is vital to increase therapeutic efficacy with minimal side effects.

Materials and methods

In the present study, we evaluated the in vitro cytotoxicity of double and triple combinations consisting of 1′S-1′-acetoxychavicol acetate (ACA), Mycobacterium indicus pranii (MIP) and cisplatin (CDDP) against 14 various human cancer cell lines to address the need for more effective therapy. Our data show synergistic effects in MCF-7 cells treated with MIP:ACA, MIP:CDDP and MIP:ACA:CDDP combinations. The type of interaction between MIP, ACA and CDDP was evaluated based on combination index being <0.8 for synergistic effect. Identifying the mechanism of cell death based on previous studies involved intrinsic apoptosis and nuclear factor kappa B (NF-κB) and tested in Western blot analysis. Inactivation of NF-κB was confirmed by p65 and IκBα, while intrinsic apoptosis pathway activation was confirmed by caspase-9 and Apaf-1 expression.

Results

All combinations confirmed intrinsic apoptosis activation and NF-κB inactivation.

Conclusion

Double and triple combination regimens that target induction of the same death mechanism with reduced dosage of each drug could potentially be clinically beneficial in reducing dose-related toxicities.

A 35-gene signature discriminates between rapidly- and slowly-progressing glioblastoma multiforme and predicts survival in known subtypes of the cancer

BACKGROUND

Gene expression can be employed for the discovery of prognostic gene or multigene signatures cancer. In this study, we assessed the prognostic value of a 35-gene expression signature selected by pathway and machine learning based methods in adjuvant therapy-linked glioblastoma multiforme (GBM) patients from the Cancer Genome Atlas.

METHODS

Genes with high expression variance was subjected to pathway enrichment analysis and those having roles in chemoradioresistance pathways were used in expression-based feature selection. A modified Support Vector Machine Recursive Feature Elimination algorithm was employed to select a subset of these genes that discriminated between rapidly-progressing and slowly-progressing patients.

RESULTS

Survival analysis on TCGA samples not used in feature selection and samples from four GBM subclasses, as well as from an entirely independent study, showed that the 35-gene signature discriminated between the survival groups in all cases (p<0.05) and could accurately predict survival irrespective of the subtype. In a multivariate analysis, the signature predicted progression-free and overall survival independently of other factors considered.

CONCLUSION

We propose that the performance of the signature makes it an attractive candidate for further studies to assess its utility as a clinical prognostic and predictive biomarker in GBM patients. Additionally, the signature genes may also be useful therapeutic targets to improve both progression-free and overall survival in GBM patients.

CISNE: An accurate description of dose-effect and synergism in combination therapies

The precise determination of dose-effect curves and the combination effect of drugs is of crucial importance in the development of new therapies for the most dreadful diseases. We have found that the current implementations of the theory of Chou et al. are not accurate enough in some circumstances and might lead to erroneous predictions of synergistic or antagonistic behaviour. We have identified the source of inaccuracies and fixed it thereby improving the accuracy of those methods. Here we explain the main features of our approach and demonstrate its higher accuracy as compared to the standard methods. Therefore, this new implementation might have a huge impact in the reliability of future research on new Combination Therapies.