Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell 'avatars' capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.

Multifunctional nanoparticle-mediated combining therapy for human diseases

Combining existing drug therapy is essential in developing new therapeutic agents in disease prevention and treatment. In preclinical investigations, combined effect of certain known drugs has been well established in treating extensive human diseases. Attributed to synergistic effects by targeting various disease pathways and advantages, such as reduced administration dose, decreased toxicity, and alleviated drug resistance, combinatorial treatment is now being pursued by delivering therapeutic agents to combat major clinical illnesses, such as cancer, atherosclerosis, pulmonary hypertension, myocarditis, rheumatoid arthritis, inflammatory bowel disease, metabolic disorders and neurodegenerative diseases. Combinatorial therapy involves combining or co-delivering two or more drugs for treating a specific disease. Nanoparticle (NP)-mediated drug delivery systems, i.e., liposomal NPs, polymeric NPs and nanocrystals, are of great interest in combinatorial therapy for a wide range of disorders due to targeted drug delivery, extended drug release, and higher drug stability to avoid rapid clearance at infected areas. This review summarizes various targets of diseases, preclinical or clinically approved drug combinations and the development of multifunctional NPs for combining therapy and emphasizes combinatorial therapeutic strategies based on drug delivery for treating severe clinical diseases. Ultimately, we discuss the challenging of developing NP-codelivery and translation and provide potential approaches to address the limitations. This review offers a comprehensive overview for recent cutting-edge and challenging in developing NP-mediated combination therapy for human diseases.

Mathematical Modeling Support for Lung Cancer Therapy-A Short Review

The paper presents a review of models that can be used to describe dynamics of lung cancer growth and its response to treatment at both cell population and intracellular processes levels. To address the latter, models of signaling pathways associated with cellular responses to treatment are overviewed. First, treatment options for lung cancer are discussed, and main signaling pathways and regulatory networks are briefly reviewed. Then, approaches used to model specific therapies are discussed. Following that, models of intracellular processes that are crucial in responses to therapies are presented. The paper is concluded with a discussion of the applicability of the presented approaches in the context of lung cancer.

Synergy Assessment of Four Antimicrobial Bioactive Compounds for the Combinational Treatment of Bacterial Pathogens

Antimicrobial resistance (AMR) has become a topic of great concern in recent years, with much effort being committed to developing alternative treatments for resistant bacterial pathogens. Drug combinational therapies have been a major area of research for several years, with modern iterations using combining well-established antibiotics and other antimicrobials with the aim of discovering complementary mechanisms. Previously, we characterised four GRAS antimicrobials that can withstand thermal polymer extrusion processes for novel medical device-based and therapeutic applications. In the present study, four antimicrobial bioactive-silver nitrate, nisin, chitosan and zinc oxide-were assessed for their potential combined use as an alternative synergistic treatment for AMR bacteria via a broth microdilution assay based on a checkerboard format. The bioactives were tested in arrangements of two-, three- and four-drug combinations, and their interactions were determined and expressed in terms of a synergy score. Results have revealed interesting interactions based on treatments against recognised test bacterial strains that cause human and animal infections, namely E. coli, S. aureus and S. epidermidis. Silver nitrate was seen to greatly enhance the efficacy of its paired treatment. Combinations with nisin, which is a lantibiotic, exhibited the most interesting results, as nisin has no effect against Gram-negative bacteria when used alone; however, it demonstrated antimicrobial effects when combined with silver nitrate or chitosan. This study constitutes the first study to both report on practical three- and four-drug combinational assays and utilise these methods for the assessment of established and emerging antimicrobials. The novel methods and results presented in this study show the potential to explore previously unknown drug combination compatibility measures in an ease-of-use- and high-throughput-based format, which can greatly help future research that aims to identify appropriate alternative treatments for AMR, including the screening of potential new bioactives biorefined from various sources.

Predicting anti-cancer drug combination responses with a temporal cell state network model

Cancer chemotherapy combines multiple drugs, but predicting the effects of drug combinations on cancer cell proliferation remains challenging, even for simple in vitro systems. We hypothesized that by combining knowledge of single drug dose responses and cell state transition network dynamics, we could predict how a population of cancer cells will respond to drug combinations. We tested this hypothesis here using three targeted inhibitors of different cell cycle states in two different cell lines in vitro. We formulated a Markov model to capture temporal cell state transitions between different cell cycle phases, with single drug data constraining how drug doses affect transition rates. This model was able to predict the landscape of all three different pairwise drug combinations across all dose ranges for both cell lines with no additional data. While further application to different cell lines, more drugs, additional cell state networks, and more complex co-culture or in vivo systems remain, this work demonstrates how currently available or attainable information could be sufficient for prediction of drug combination response for single cell lines in vitro.

PD-L1 antibody enhanced β-glucan antitumor effects via blockade of the immune checkpoints in a melanoma model

In the tumor microenvironment (TME), one of the major functions of tumor-recruited CD11b⁺ cells are the suppression of the T-cell-mediated anti-tumor immune response. β-glucan could convert the phenotype of tumor-recruited CD11b⁺ cells from the suppressive to the promotive, and enhanced their anti-tumor effects. However, β-glucan could enhance the PD-1/PD-L1 expression on CD11b⁺ cells, while PD-1 could inhibit macrophage phagocytosis and PD-L1 could induce a co-inhibitory signal in T-cells and lead to T-cell apoptosis and anergy. These protumor effects may be reversed by PD-1/PD-L1 block therapy. In the present study, we focused on the efficacy of β-glucan anti-tumor therapy combined with anti-PD-L1 mAb treatment, and the mechanism of their synergistic effects could be fully verified. We verified the effect of β-glucan (i.e., inflammatory cytokine secretion of TNF-α, IL-12, IL-6, IL-1β and the expression of immune checkpoint PD-1/PD-L1) in naïve mouse peritoneal exudate CD11b⁺ cells. In our mouse melanoma model, treatment with a PD-L1 blocking antibody with β-glucan synergized tumor regression. After treatment with β-glucan and anti-PD-L1 mAb antibody, tumor infiltrating leukocyte (TILs) not only showed a competent T-cell function (CD107a, perforin, IL-2, IFN-γ and Ki67) and CTL population, but also showed enhanced tumor-recruited CD11b⁺ cell activity (IL-12, IL-6, IL-1β and PD-1). This effect was also verified in the peritoneal exudate CD11b⁺ cells of tumor-bearing mice. PD-1/PD-L1 blockade therapy enhanced the β-glucan antitumor effects via the blockade of tumor-recruited CD11b⁺ cell immune checkpoints in the melanoma model.

Dynamical Synergy of Drug Combinations during Cancer Chemotherapy

Synergistic drug combinations often provide effective strategies to increase treatment efficacy and, during therapy, it is a time-dependent process. Data for colorectal and lung cancer in vivo were used for the phenomenological study of dynamical synergy during treatments. The proposed approach takes into consideration tumor regrowth by macroscopic laws. The time dependencies of synergistic drug combinations are analyzed by different parametric indicators. The cumulative effects of the single therapy and drug combinations are quantitatively well described and related to the cumulative doses. In conclusion, the analysis of dynamical synergy during chemotherapy has to take into account the effects of the drug doses and the tumor regrowth, which can provide a reliable description of the synergistic time dependence.

Synergistic protective effects of folic acid and resveratrol against fine particulate matter-induced heart malformations in zebrafish embryos

Ambient fine particulate matter (PM2.5) is a major environmental health problem worldwide, and recent studies indicate that maternal PM2.5 exposure is closely associated with congenital heart diseases (CHDs) in offspring. We previously found that supplementation with folic acid (FA) or Resveratrol (RSV) could protect against heart defects in zebrafish embryos exposed to extractable organic matter (EOM) from PM2.5 by targeting aryl hydrocarbon receptor (AHR) signaling and reactive oxygen species (ROS) production respectively. Thus, we hypothesized that FA combined with RSV may have a synergistic protective effect against PM2.5-induced heart defects. To test our hypothesis, we treated zebrafish embryos with EOM in the presence or absence of FA, RSV or a combination of both. We found that RSV and FA showed a clear synergistic protection against EOM-induced heart defects in zebrafish embryos. Further studies showed that FA and RSV suppressed EOM-induced AHR activity and ROS generation respectively. Although only RSV inhibited EOM-induced apoptosis, FA enhanced the inhibitory effect of RSV. Moreover, vitamin C (VC), a typical antioxidant, also exhibits a synergistic inhibitory effect with FA on EOM-induced apoptosis and heart defects. In conclusion, supplementation with FA and RSV have a synergistic protective effect against PM2.5-induced heart defects in zebrafish embryos by targeting AHR activity and ROS production respectively. Our results indicate that, in the presence of antioxidants, FA even at a low concentration level could protect against the high risk of CHDs caused by air pollution.

Potential Role of Bioactive Phytochemicals in Combination Therapies against Antimicrobial Activity

Since ancient times, plants have been a major source of novel drug molecules and have been used in the treatment of different infectious diseases. Secondary plant metabolites have miraculous healing properties and show potent therapeutic responses when used in combination drug therapy. The prime objective of this review is to summarize the concept of drug combination with special emphasis on the synergistic interactions between plant-derived bioactive phytochemicals with commercially available antimicrobial agents. The study also assesses the roles, importance, and applicability of phytochemicals in the management of different diseases. The review focuses on different aspects of combined antimicrobial activities, the possible mechanisms involved, and the current status of research in the field. The study was conducted based on an extensive literature survey that resulted in the following hypothesis: secondary metabolites derived from plants possess remarkable therapeutic activities. The study was designed as a systematic review that ensures unbiased and accurate representations of the relevant data and information. Jadad scale selection criteria were used for qualitative analysis of the articles to assess them based on the relevant secure score (minimum and maximum scores range between 1 and 5, respectively). Articles with secure scores > 3 were considered for the study. A comprehensive literature survey was conducted using resource databases including PubMed, Google Scholar, Bielefeld Academic Search Engine, Research Gate, Scopus, Medline, and Science Direct up to June 2019. This article contains concise information about the most commonly used bioactive phytochemicals with potent antifungal and antibacterial effects.

Synergistic antitumor effect of a penicillin derivative combined with thapsigargin in melanoma cells

PURPOSE

To investigate the effect of TAP7f, a penicillin derivative previously characterized as a potent antitumor agent that promotes ER stress and apoptosis, in combination with thapsigargin, an ER stress inducer, on melanoma cells.

METHODS

The synergistic antiproliferative effect of TAP7f in combination with thapsigargin was studied in vitro in murine B16-F0 melanoma cells, and in human A375 and SB2 melanoma cells. In vivo assays were performed with C57BL/6J mice challenged with B16-F0 cells. Immunofluorescence and Western blot assays were carried out to characterize the induction of ER stress and apoptosis. Necrotic tumor areas and the potential toxicity of the combined therapy were examined by histological analysis of tissue sections after hematoxylin-eosin staining.

RESULTS

In vitro, the combination of TAP7f with thapsigargin synergistically inhibited the proliferation of murine B16-F0, and human A375 and SB2 melanoma cells. When non-inhibitory doses of each drug were simultaneously administered to C57BL/6J mice challenged with B16-F0 cells, a 50% reduction in tumor volumes was obtained in the combined group. An apoptotic response characterized by higher expression levels of Baxenhanced PARP-1 cleavage and the presence of active caspase 3 was observed in tumors from the combined treatment. In addition, higher expression levels of GADD153/CHOP and ATF4 were found in tumors of mice treated with both drugs with respect to each drug used alone, indicating the induction of an ER stress response. No signs of tissue toxicity were observed in histological sections of different organs extracted from mice receiving the combination.

CONCLUSION

The synergistic and effective antitumor action of TAP7f in combination with thapsigargin could be considered as a potential therapeutic strategy for melanoma treatment.

Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer

Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.

OBIF: an omics-based interaction framework to reveal molecular drivers of synergy

Bioactive molecule library screening may empirically identify effective combination therapies, but molecular mechanisms underlying favorable drug–drug interactions often remain unclear, precluding further rational design. In the absence of an accepted systems theory to interrogate synergistic responses, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in synergistic biological responses. OBIF performs full factorial analysis of feature expression data from single versus dual exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed transcriptomic and proteomic data of a dyad of immunostimulatory molecules that induces synergistic protection against influenza A and revealed unanticipated NF-κB/AP-1 cooperation that is required for antiviral protection. To demonstrate generalizability, OBIF analyzed data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, unlike existing synergy quantification and prediction methods, OBIF is a phenotype-driven systems model that supports multiplatform interrogation of synergy mechanisms.

Mitogen Synergy: An Emerging Route to Boosting Human Beta Cell Proliferation

Decreased number and function of beta cells are a key aspect of diabetes mellitus (diabetes), a disease that remains an onerous global health problem. Means of restoring beta cell mass are urgently being sought as a potential cure for diabetes. Several strategies, such as de novo beta cell derivation via pluripotent stem cell differentiation or mature somatic cell transdifferentiation, have yielded promising results. Beta cell expansion is another promising strategy, rendered challenging by the very low proliferative capacity of beta cells. Many effective mitogens have been identified in rodents, but the vast majority do not have similar mitogenic effects in human beta cells. Extensive research has led to the identification of several human beta cell mitogens, but their efficacy and specificity remain insufficient. An approach based on the simultaneous application of several mitogens has recently emerged and can yield human beta cell proliferation rates of up to 8%. Here, we discuss recent advances in restoration of the beta cell population, focusing on mitogen synergy, and the contribution of RNA-sequencing (RNA-seq) to accelerating the elucidation of signaling pathways in proliferating beta cells and the discovery of novel mitogens. Together, these approaches have taken beta cell research up a level, bringing us closer to a cure for diabetes.

The Combined Treatment With the FLT3-Inhibitor AC220 and the Complex I Inhibitor IACS-010759 Synergistically Depletes Wt- and FLT3-Mutated Acute Myeloid Leukemia Cells

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy with a high mortality rate and relapse risk. Although progress on the genetic and molecular understanding of this disease has been made, the standard of care has changed minimally for the past 40 years and the five-year survival rate remains poor, warranting new treatment strategies. Here, we applied a two-step screening platform consisting of a primary cell viability screening and a secondary metabolomics-based phenotypic screening to find synergistic drug combinations to treat AML. A novel synergy between the oxidative phosphorylation inhibitor IACS-010759 and the FMS-like tyrosine kinase 3 (FLT3) inhibitor AC220 (quizartinib) was discovered in AML and then validated by ATP bioluminescence and apoptosis assays. In-depth stable isotope tracer metabolic flux analysis revealed that IACS-010759 and AC220 synergistically reduced glucose and glutamine enrichment in glycolysis and the TCA cycle, leading to impaired energy production and de novo nucleotide biosynthesis. In summary, we identified a novel drug combination, AC220 and IACS-010759, which synergistically inhibits cell growth in AML cells due to a major disruption of cell metabolism, regardless of FLT3 mutation status.

Alpelisib and radiotherapy treatment enhances Alisertib-mediated cervical cancer tumor killing

Human papilloma virus (HPV) is the main causative agent in cervical cancers. High-risk HPV cancers, including cervical cancer, are driven by major HPV oncogene, E6 and E7, which promote uncontrolled cell growth and genomic instability. We have previously shown that the presence of HPV E7 sensitizes cells to inhibition of aurora kinases (AURKs), which regulates the control of cell entry into and through mitosis. Such treatment is highly effective at eliminating early tumors and reducing large, late tumors. In addition, the presence of HPV oncogenes also sensitizes cells to inhibition of phosphoinositide 3-kinases (PI3Ks), a family of enzymes involved in cellular functions such as cell growth and proliferation. Using MLN8237 (Alisertib), an oral, selective inhibitor of AURKs, we investigated whether Alisertib treatment can improve tumor response when combined with either radiotherapy (RT) treatment or with a PI3K inhibitor, BYL719 (Alpelisib). Indeed, both RT and Alpelisib significantly improved Alisertib-mediated tumor killing, and the promising achieved results warrant further development of these combinations, and potentially translating them to the clinics.

Extending the lymphoblastoid cell line model for drug combination pharmacogenomics

Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.

Polymeric Nanoparticle Delivery of Combination Therapy with Synergistic Effects in Ovarian Cancer

Treatment of ovarian cancer is challenging due to late stage diagnosis, acquired drug resistance mechanisms, and systemic toxicity of chemotherapeutic agents. Combination chemotherapy has the potential to enhance treatment efficacy by activation of multiple downstream pathways to overcome drug resistance and reducing required dosages. Sequence of delivery and the dosing schedule can further enhance treatment efficacy. Formulation of drug combinations into nanoparticles can further enhance treatment efficacy. Due to their versatility, polymer-based nanoparticles are an especially promising tool for clinical translation of combination therapies with tunable dosing schedules. We review polymer nanoparticle (e.g., micelles, dendrimers, and lipid nanoparticles) carriers of drug combinations formulated to treat ovarian cancer. In particular, the focus on this review is combinations of platinum and taxane agents (commonly used first line treatments for ovarian cancer) combined with other small molecule therapeutic agents. In vitro and in vivo drug potency are discussed with a focus on quantifiable synergistic effects. The effect of drug sequence and dosing schedule is examined. Computational approaches as a tool to predict synergistic drug combinations and dosing schedules as a tool for future nanoparticle design are also briefly discussed.

Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy

In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.

Predicting drug synergy for precision medicine using network biology and machine learning

Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. the synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

Synergistic drug combinations prediction by integrating pharmacological data

There is compelling evidence that synergistic drug combinations have become promising strategies for combating complex diseases, and they have evident predominance comparing to traditional one drug - one disease approaches. In this paper, we develop a computational method, namely SyFFM, that takes pharmacological data into consideration and applies field-aware factorization machines to analyze and predict potential synergistic drug combinations. Firstly, features of drug pairs are constructed based on associations between drugs and target, and enzymes, and indication areas. Then, the synergistic scores of drug combinations are obtained by implementing field-aware factorization machines on latent vector space of these features. Finally, synergistic combinations can be predicted by introducing a threshold. We applied SyFFM to predict pairwise synergistic combinations and three-drug synergistic combinations, and the performance is good in terms of cross-validation. Besides, more than 90% combinations of the top ranked predictions are proved by literature and the analysis of parameters in model shows that our method can help to investigate and explain synergistic mechanisms underlying combinatorial therapy.

Predicting combinative drug pairs via multiple classifier system with positive samples only

BACKGROUND AND OBJECTIVE

Due to the synergistic effects of drugs, drug combination is one of the effective approaches for treating complex diseases. However, the identification of drug combinations by dose-response methods is still costly. It is promising to develop supervised learning-based approaches to predict potential drug combinations on a large scale. Nevertheless, these approaches have the inadequate utilization of heterogeneous features, which causes the loss of information useful to classification. Moreover, they have an intrinsic bias, because they assume unknown drug pairs as non-combinations, of which some could be real drug combinations in practice.

METHODS

To address above issues, this work first designs a two-layer multiple classifier system (TLMCS) to effectively integrate heterogeneous features involving anatomical therapeutic chemical codes of drugs, drug-drug interactions, drug-target interactions, gene ontology of drug targets, and side effects. To avoid the bias caused by labelling unknown samples as negative, it then utilizes the one-class support vector machines, (which requires no negative instance and only labels approved drug combinations as positive instances), as the member classifiers in TLMCS. Last, both a 10-fold cross validation (10-CV) and a novel prediction are performed to validate the performance of TLMCS.

RESULTS

The comparison with three state-of-the-art approaches under 10-CV exhibits the superiority of TLMCS, which achieves the area under the receiver operating characteristic curve = 0.824 and the area under the precision-recall curve = 0.372. Moreover, the experiment under the novel prediction demonstrates its ability, where 9 out of the top-20 predicted combinative drug pairs are validated by checking the published literature. Furthermore, for each of the newly-validated drug combinations, this work analyses the combining mode of the member drugs and investigates their relationship in terms of drug targeting pathways.

CONCLUSIONS

The proposed TLMCS provides an effective framework to integrate those heterogeneous features and is trained by only positive samples such that the bias of taking unknown drug pairs as negative samples can be avoided. Furthermore, its results in the novel prediction reveal five types of drug combinations and three types of drug relationships in terms of pathways.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes

Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space. Most diseases are not caused by a single pathogenic factor, but by multiple disease genes, in particular, interacting disease genes. Thus, it is reasonable to consider that targeting epistatic disease genes may enhance the therapeutic effects of combinatorial drugs. In this study, synthetic lethality gene pairs of tumors, similar to epistatic disease genes, were first targeted by combinatorial drugs, resulting in the enrichment of the combinatorial drugs with cancer treatment, which verified our hypothesis. Then, conventional epistasis detection software was used to identify epistatic disease genes from the genome wide association studies (GWAS) dataset. Furthermore, combinatorial drugs were predicted by targeting these epistatic disease genes, and five combinations were proven to have synergistic anti-cancer effects on MCF-7 cells through cell cytotoxicity assay. Combined with the three-dimensional (3D) genome-based method, the epistatic disease genes were filtered and were more closely related to disease. By targeting the filtered gene pairs, the efficiency of combinatorial drug discovery has been further improved.

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.

Bacteriocin-Antimicrobial Synergy: A Medical and Food Perspective

The continuing emergence of multi-drug resistant pathogens has sparked an interest in seeking alternative therapeutic options. Antimicrobial combinatorial therapy is one such avenue. A number of studies have been conducted, involving combinations of bacteriocins with other antimicrobials, to circumvent the development of antimicrobial resistance and/or increase antimicrobial potency. Such bacteriocin-antimicrobial combinations could have tremendous value, in terms of reducing the likelihood of resistance development due to the involvement of two distinct mechanisms of antimicrobial action. Furthermore, antimicrobial synergistic interactions may also have potential financial implications in terms of decreasing the costs of treatment by reducing the concentration of an expensive antimicrobial and utilizing it in combination with an inexpensive one. In addition, combinatorial therapies with bacteriocins can broaden antimicrobial spectra and/or result in a reduction in the concentration of an antibiotic required for effective treatments to the extent that potentially toxic or adverse side effects can be reduced or eliminated. Here, we review studies in which bacteriocins were found to be effective in combination with other antimicrobials, with a view to targeting clinical and/or food-borne pathogens. Furthermore, we discuss some of the bottlenecks which are currently hindering the development of bacteriocins as viable therapeutic options, as well as addressing the need to exercise caution when attempting to predict clinical outcomes of bacteriocin-antimicrobial combinations.

Transcriptome and network analyses in Saccharomyces cerevisiae reveal that amphotericin B and lactoferrin synergy disrupt metal homeostasis and stress response

Invasive fungal infections are difficult to treat. The few available antifungal drugs have problems with toxicity or efficacy, and resistance is increasing. To overcome these challenges, existing therapies may be enhanced by synergistic combination with another agent. Previously, we found amphotericin B (AMB) and the iron chelator, lactoferrin (LF), were synergistic against a range of different fungal pathogens. This study investigates the mechanism of AMB-LF synergy, using RNA-seq and network analyses. AMB treatment resulted in increased expression of genes involved in iron homeostasis and ATP synthesis. Unexpectedly, AMB-LF treatment did not lead to increased expression of iron and zinc homeostasis genes. However, genes involved in adaptive response to zinc deficiency and oxidative stress had decreased expression. The clustering of co-expressed genes and network analysis revealed that many iron and zinc homeostasis genes are targets of transcription factors Aft1p and Zap1p. The aft1Δ and zap1Δ mutants were hypersensitive to AMB and H₂O₂, suggesting they are key regulators of the drug response. Mechanistically, AMB-LF synergy could involve AMB affecting the integrity of the cell wall and membrane, permitting LF to disrupt intracellular processes. We suggest that Zap1p- and Aft1p-binding molecules could be combined with existing antifungals to serve as synergistic treatments.

Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

Current Trends in Multidrug Optimization

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.