Applying synergy metrics to combination screening data: agreements, disagreements and pitfalls

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.

Triple Blockade of Oncogenic RAS Signaling Using KRAS and MEK Inhibitors in Combination with Irradiation in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest of human malignancies and carries an exceptionally poor prognosis. It is mostly driven by multiple oncogenic alterations, with the highest mutation frequency being observed in the KRAS gene, which is a key oncogenic driver of tumorogenesis and malignant progression in PDAC. However, KRAS remained undruggable for decades until the emergence of G12C mutation specific KRAS inhibitors. Despite this development, this therapeutic approach to target KRAS directly is not routinely used for PDAC patients, with the reasons being the rare presence of G12C mutation in PDAC with only 1-2% of occurring cases, modest therapeutic efficacy, activation of compensatory pathways leading to cell resistance, and absence of effective KRASG12D or pan-KRAS inhibitors. Additionally, indirect approaches to targeting KRAS through upstream and downstream regulators or effectors were also found to be either ineffective or known to cause major toxicities. For this reason, new and more effective treatment strategies that combine different therapeutic modalities aiming at achieving synergism and minimizing intrinsic or adaptive resistance mechanisms are required. In the current work presented here, pancreatic cancer cell lines with oncogenic KRAS G12C, G12D, or wild-type KRAS were treated with specific KRAS or SOS1/2 inhibitors, and therapeutic synergisms with concomitant MEK inhibition and irradiation were systematically evaluated by means of cell viability, 2D-clonogenic, 3D-anchorage independent soft agar, and bioluminescent ATP assays. Underlying pathophysiological mechanisms were examined by using Western blot analyses, apoptosis assay, and RAS activation assay.

SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction

Drug combination therapies are well-established strategies for the treatment of cancer with low toxicity and fewer adverse effects. Computational drug synergy prediction approaches can accelerate the discovery of novel combination therapies, but the existing methods do not explicitly consider the key role of important substructures in producing synergistic effects. To this end, we propose a significant substructure-aware anticancer drug synergy prediction method, named SDDSynergy, to adaptively identify critical functional groups in drug synergy. SDDSynergy splits the task of predicting drug synergy into predicting the effect of individual substructures on cancer cell lines and highlights the impact of important substructures through a novel drug-cell line attention mechanism. And a substructure pair attention mechanism is incorporated to capture the information on internal substructure pairs interaction in drug combinations, which aids in predicting synergy. The substructures of different sizes and shapes are directly obtained from the molecular graph of the drugs by multilayer substructure information passing networks. Extensive experiments on three real-world data sets demonstrate that SDDSynergy outperforms other state-of-the-art methods. We also verify that many of the novel drug combinations predicted by SDDSynergy are supported by previous studies or clinical trials through an in-depth literature survey.

Synergy detection: A practical guide to statistical assessment of potential drug combinations

Combination treatments have been of increasing importance in drug development across therapeutic areas to improve treatment response, minimize the development of resistance, and/or minimize adverse events. Pre-clinical in-vitro combination experiments aim to explore the potential of such drug combinations during drug discovery by comparing the observed effect of the combination with the expected treatment effect under the assumption of no interaction (i.e., null model). This tutorial will address important design aspects of such experiments to allow proper statistical evaluation. Additionally, it will highlight the Biochemically Intuitive Generalized Loewe methodology (BIGL R package available on CRAN) to statistically detect deviations from the expectation under different null models. A clear advantage of the methodology is the quantification of the effect sizes, together with confidence interval while controlling the directional false coverage rate. Finally, a case study will showcase the workflow in analyzing combination experiments.

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.

Interactions of plumbagin with five common antibiotics against Staphylococcus aureus in vitro

Staphylococcus aureus is the main culprit, causing a variety of severe clinical infections. At the same time, clinics are also facing the severe situation of antibiotic resistance. Therefore, effective strategies to address this problem may include expanding the antimicrobial spectrum by exploring alternative sources of drugs or delaying the development of antibiotic resistance through combination therapy so that existing antibiotics can continue to be used. Plumbagin (PLU) is a phytochemical that exhibits antibacterial activity. In the present study, we investigated the in vitro antibacterial activity of PLU. We selected five antibiotics with different mechanisms and inhibitory activities against S. aureus to explore their interaction with the combination of PLU. The interaction of combinations was evaluated by the Bliss independent model and visualized through response surface analysis. PLU exhibited potent antibacterial activity, with half maximal inhibitory concentration (IC50) and minimum inhibitory concentration (MIC) values against S. aureus of 1.73 μg/mL and 4 μg/mL, respectively. Synergism was observed when PLU was combined with nitrofurantoin (NIT), ciprofloxacin (CPR), mecillinam (MEC), and chloramphenicol (CHL). The indifference of the trimethoprim (TMP)-PLU pairing was demonstrated across the entire dose-response matrix, but significant synergy was observed within a specific dose region. In addition, no antagonistic interactions were indicated. Overall, PLU is not only a promising antimicrobial agent but also has the potential to enhance the growth-inhibitory activity of some antibiotics against S. aureus, and the use of the interaction landscape, along with the dose-response matrix, for analyzing and quantifying combination results represents an improved approach to comprehending antibacterial combinations.

Guiding model-driven combination dose selection using multi-objective synergy optimization

Despite the growing appreciation that the future of cancer treatment lies in combination therapies, finding the right drugs to combine and the optimal way to combine them remains a nontrivial task. Herein, we introduce the Multi-Objective Optimization of Combination Synergy - Dose Selection (MOOCS-DS) method for using drug synergy as a tool for guiding dose selection for a combination of preselected compounds. This method decouples synergy of potency (SoP) and synergy of efficacy (SoE) and identifies Pareto optimal solutions in a multi-objective synergy space. Using a toy combination therapy model, we explore properties of the MOOCS-DS algorithm, including how optimal dose selection can be influenced by the metric used to define SoP and SoE. We also demonstrate the potential of our approach to guide dose and schedule selection using a model fit to preclinical data of the combination of the PD-1 checkpoint inhibitor pembrolizumab and the anti-angiogenic drug bevacizumab on two lung cancer cell lines. The identification of optimally synergistic combination doses has the potential to inform preclinical experimental design and improve the success rates of combination therapies. Jel classificationDose Finding in Oncology.

Systematic analysis of drug combinations against Gram-positive bacteria

Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled ~8,000 combinations of 65 antibacterial drugs against the model species Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface ( https://apps.embl.de/combact/ ).

Statistical Assessment of Drug Synergy from In Vivo Combination Studies Using Mouse Tumor Models

Drug combination therapy is a promising strategy for treating cancer; however, its efficacy and synergy require rigorous evaluation in preclinical studies before going to clinical trials. Existing methods have limited power to detect synergy in animal studies. Here, we introduce a novel approach to assess in vivo drug synergy with high sensitivity and low false discovery rate. It can accurately estimate combination index and synergy score under the Bliss independence model and the highest single agent (HSA) model without any assumption on tumor growth kinetics, study duration, data completeness and balance for tumor volume measurement. We show that our method can effectively validate in vitro drug synergy discovered from cell line assays in in vivo xenograft experiments, and can help to elucidate the mechanism of action for immune checkpoint inhibitors in syngeneic mouse models by combining an anti-PD-1 antibody and several tumor-infiltrating leukocytes depletion treatments. We provide a unified view of in vitro and in vivo synergy by presenting a parallelism between the fixed-dose in vitro and the 4-group in vivo combination studies, so they can be better designed, analyzed, and compared. We emphasize that combination index, when defined here via relative survival of tumor cells, is both dose and time dependent, and give guidelines on designing informative in vivo combination studies. We explain how to interpret and apply Bliss and HSA synergies. Finally, we provide an open-source software package named invivoSyn that enables automated analysis of in vivo synergy using our method and several other existing methods.

SIGNIFICANCE

This work presents a general solution to reliably determine in vivo drug synergy in single-dose 4-group animal combination studies.

AADB: A Manually Collected Database for Combinations of Antibiotics With Adjuvants

Antimicrobial resistance is a global public health concern. The lack of innovations in antibiotic development has led to renewed interest in antibiotic adjuvants. However, there is no database to collect antibiotic adjuvants. Herein, we build a comprehensive database named Antibiotic Adjuvant DataBase (AADB) by manually collecting relevant literature. Specifically, AADB includes 3,035 combinations of antibiotics with adjuvants, covering 83 antibiotics, 226 adjuvants, and 325 bacterial strains. AADB provides user-friendly interfaces for searching and downloading. Users can easily obtain these datasets for further analysis. In addition, we also collected related datasets (e.g., chemogenomic and metabolomic data) and proposed a computational strategy to dissect these datasets. As a test case, we identified 10 candidates for minocycline, and 6 of 10 candidates are the known adjuvants that synergize with minocycline to inhibit the growth of E. coli BW25113. We hope that AADB can help users to identify effective antibiotic adjuvants. AADB is freely available at http://www.acdb.plus/AADB.

Class I HDAC inhibition reduces DNA damage repair capacity of MYC-amplified medulloblastoma cells

PURPOSE

MYC-driven Group 3 medulloblastoma (MB) (subtype II) is a highly aggressive childhood brain tumor. Sensitivity of MYC-driven MB to class I histone deacetylase inhibitors (HDACi) has been previously demonstrated in vitro and in vivo. In this study we characterize the transcriptional effects of class I HDACi in MYC-driven MB and explore beneficial drug combinations.

METHODS

MYC-amplified Group 3 MB cells (HD-MB03) were treated with class I HDACi entinostat. Changes in the gene expression profile were quantified on a microarray. Bioinformatic assessment led to the identification of pathways affected by entinostat treatment. Five drugs interfering with these pathways (olaparib, idasanutlin, ribociclib, selinexor, vinblastine) were tested for synergy with entinostat in WST-8 metabolic activity assays in a 5 × 5 combination matrix design. Synergy was validated in cell count and flow cytometry experiments. The effect of entinostat and olaparib on DNA damage was evaluated by γH2A.X quantification in immunoblotting, fluorescence microscopy and flow cytometry.

RESULTS

Entinostat treatment changed the expression of genes involved in 22 pathways, including downregulation of DNA damage response. The PARP1 inhibitors olaparib and pamiparib showed synergy with entinostat selectively in MYC-amplified MB cells, leading to increased cell death, decreased viability and increased formation of double strand breaks, as well as increased sensitivity to additional induction of DNA damage by doxorubicin. Non-MYC-amplified MB cells and normal human fibroblasts were not susceptible to this triple treatment.

CONCLUSION

Our study identifies the combination of entinostat with olaparib as a new potential therapeutic approach for MYC-driven Group 3 MB.

The recent progress of deep-learning-based in silico prediction of drug combination

Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

Synergism: biocontrol agents and biostimulants in reducing abiotic and biotic stresses in crop

In today's fast-shifting climate change scenario, crops are exposed to environmental pressures, abiotic and biotic stress. Hence, these will affect the production of agricultural products and give rise to a worldwide economic crisis. The increase in world population has exacerbated the situation with increasing food demand. The use of chemical agents is no longer recommended due to adverse effects towards the environment and health. Biocontrol agents (BCAs) and biostimulants, are feasible options for dealing with yield losses induced by plant stresses, which are becoming more intense due to climate change. BCAs and biostimulants have been recommended due to their dual action in reducing both stresses simultaneously. Although protection against biotic stresses falls outside the generally accepted definition of biostimulant, some microbial and non-microbial biostimulants possess the biocontrol function, which helps reduce biotic pressure on crops. The application of synergisms using BCAs and biostimulants to control crop stresses is rarely explored. Currently, a combined application using both agents offer a great alternative to increase the yield and growth of crops while managing stresses. This article provides an overview of crop stresses and plant stress responses, a general knowledge on synergism, mathematical modelling used for synergy evaluation and type of in vitro and in vivo synergy testing, as well as the application of synergism using BCAs and biostimulants in reducing crop stresses. This review will facilitate an understanding of the combined effect of both agents on improving crop yield and growth and reducing stress while also providing an eco-friendly alternative to agroecosystems.

Biochemical and Structural Characterization of a Peptidic Inhibitor of the YAP:TEAD Interaction That Binds to the α-Helix Pocket on TEAD

The TEAD transcription factors are the most distal elements of the Hippo pathway, and their transcriptional activity is regulated by several proteins, including YAP. In some cancers, the Hippo pathway is deregulated and inhibitors of the YAP:TEAD interaction are foreseen as new anticancer drugs. The binding of YAP to TEAD is driven by the interaction of an α-helix and an Ω-loop present in its TEAD-binding domain with two distinct pockets at the TEAD surface. Using the mRNA-based display technique to screen a library of in vitro-translated cyclic peptides, we identified a peptide that binds with a nanomolar affinity to TEAD. The X-ray structure of this peptide in complex with TEAD reveals that it interacts with the α-helix pocket. Under our experimental conditions, this peptide can form a ternary complex with TEAD and YAP. Furthermore, combining it with a peptide binding to the Ω-loop pocket gives an additive inhibitory effect on the YAP:TEAD interaction. Overall, our results show that it is possible to identify nanomolar inhibitors of the YAP:TEAD interaction that bind to the α-helix pocket, suggesting that developing such compounds might be a strategy to treat cancers where the Hippo pathway is deregulated.

Ultrasensitive response explains the benefit of combination chemotherapy despite drug antagonism

Most aggressive lymphomas are treated with combination chemotherapy, commonly as multiple cycles of concurrent drug administration. Concurrent administration is in theory optimal when combination therapies have synergistic (more than additive) drug interactions. We investigated pharmacodynamic interactions in the standard 4-drug 'CHOP' regimen in Peripheral T-Cell Lymphoma (PTCL) cell lines, and found that CHOP consistently exhibits antagonism and not synergy. We tested whether staggered treatment schedules could improve tumor cell kill by avoiding antagonism, using month-long in vitro models of concurrent or staggered treatments. Surprisingly, we observed that tumor cell kill is maximized by concurrent drug administration despite antagonistic drug-drug interactions. We propose that an ultrasensitive dose response, as described in radiology by the linear-quadratic (LQ) model, can reconcile these seemingly contradictory experimental observations. The LQ model describes the relationship between cell survival and dose, and in radiology has identified scenarios favoring hypofractionated radiation - the administration of fewer large doses rather than multiple smaller doses. Specifically, hypofractionated treatment can be favored when cells require an accumulation of DNA damage, rather than a 'single hit', in order to die. By adapting the LQ model to combination chemotherapy and accounting for tumor heterogeneity, we find that tumor cell kill is maximized by concurrent administration of multiple drugs, even when chemotherapies have antagonistic interactions. Thus, our study identifies a new mechanism by which combination chemotherapy can be clinically beneficial that is not reliant on positive drug-drug interactions.

Chemopreventive mechanisms of amentoflavone: recent trends and advancements

In parallel to the continuous rise of new cancer cases all over the world, the interest of scientific community in natural anticancer agents has steadily been increased. In the past decades, numerous phytochemicals have been shown to possess a strong anticancer potential in preclinical conditions. One of such interesting compounds, derived from different plants such as ginkgo, hinoki, and St. John`s wort, is amentoflavone. In this review article, a wide range of anticancer properties of this natural biflavone are described, revealing its ability to suppress the malignant growth and lead tumor cells to apoptotic death, besides impeding also angiogenic and metastatic processes. Therefore, amentoflavone can be considered a potential lead compound for the development of novel anticancer drug candidates, definitely deserving further in vivo studies and also initiation of clinical trials. It is expected that this plant biflavone might be important, either alone or in combination with the current standard chemotherapeutics, in providing some alleviation for the continuous rise of global cancer burden.

Combined inhibition of EZH2 and ATM is synthetic lethal in BRCA1-deficient breast cancer

Background

The majority of BRCA1-mutant breast cancers are characterized by a triple-negative phenotype and a basal-like molecular subtype, associated with aggressive clinical behavior. Current treatment options are limited, highlighting the need for the development of novel targeted therapies for this tumor subtype.

Methods

Our group previously showed that EZH2 is functionally relevant in BRCA1-deficient breast tumors and blocking EZH2 enzymatic activity could be a potent treatment strategy. To validate the role of EZH2 as a therapeutic target and to identify new synergistic drug combinations, we performed a high-throughput drug combination screen in various cell lines derived from BRCA1-deficient and -proficient mouse mammary tumors.

Results

We identified the combined inhibition of EZH2 and the proximal DNA damage response kinase ATM as a novel synthetic lethality-based therapy for the treatment of BRCA1-deficient breast tumors. We show that the combined treatment with the EZH2 inhibitor GSK126 and the ATM inhibitor AZD1390 led to reduced colony formation, increased genotoxic stress, and apoptosis-mediated cell death in BRCA1-deficient mammary tumor cells in vitro. These findings were corroborated by in vivo experiments showing that simultaneous inhibition of EZH2 and ATM significantly increased anti-tumor activity in mice bearing BRCA1-deficient mammary tumors.

Conclusion

Taken together, we identified a synthetic lethal interaction between EZH2 and ATM and propose this synergistic interaction as a novel molecular combination for the treatment of BRCA1-mutant breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01534-y.

SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.

SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples

SynergyFinder (https://synergyfinder.fimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose combination data analytics, partly because the development of its functionality and graphical interface has been driven by a diverse user community, including both chemical biologists and computational scientists. Here, we describe the latest upgrade of this community-effort, SynergyFinder release 3.0, introducing a number of novel features that support interactive multi-sample analysis of combination synergy, a novel consensus synergy score that combines multiple synergy scoring models, and an improved outlier detection functionality that eliminates false positive results, along with many other post-analysis options such as weighting of synergy by drug concentrations and distinguishing between different modes of synergy (potency and efficacy). Based on user requests, several additional improvements were also implemented, including new data visualizations and export options for multi-drug combinations. With these improvements, SynergyFinder 3.0 supports robust identification of consistent combinatorial synergies for multi-drug combinatorial discovery and clinical translation.

The context-dependent, combinatorial logic of BMP signaling

Cell-cell communication systems typically comprise families of ligand and receptor variants that function together in combinations. Pathway activation depends on the complex way in which ligands are presented extracellularly and receptors are expressed by the signal-receiving cell. To understand the combinatorial logic of such a system, we systematically measured pairwise bone morphogenetic protein (BMP) ligand interactions in cells with varying receptor expression. Ligands could be classified into equivalence groups based on their profile of positive and negative synergies with other ligands. These groups varied with receptor expression, explaining how ligands can functionally replace each other in one context but not another. Context-dependent combinatorial interactions could be explained by a biochemical model based on the competitive formation of alternative signaling complexes with distinct activities. Together, these results provide insights into the roles of BMP combinations in developmental and therapeutic contexts and establish a framework for analyzing other combinatorial, context-dependent signaling systems.

SRDFM: Siamese Response Deep Factorization Machine to improve anti-cancer drug recommendation

Predicting the response of cancer patients to a particular treatment is a major goal of modern oncology and an important step toward personalized treatment. In the practical clinics, the clinicians prefer to obtain the most-suited drugs for a particular patient instead of knowing the exact values of drug sensitivity. Instead of predicting the exact value of drug response, we proposed a deep learning-based method, named Siamese Response Deep Factorization Machines (SRDFM) Network, for personalized anti-cancer drug recommendation, which directly ranks the drugs and provides the most effective drugs. A Siamese network (SN), a type of deep learning network that is composed of identical subnetworks that share the same architecture, parameters and weights, was used to measure the relative position (RP) between drugs for each cell line. Through minimizing the difference between the real RP and the predicted RP, an optimal SN model was established to provide the rank for all the candidate drugs. Specifically, the subnetwork in each side of the SN consists of a feature generation level and a predictor construction level. On the feature generation level, both drug property and gene expression, were adopted to build a concatenated feature vector, which even enables the recommendation for newly designed drugs with only chemical property known. Particularly, we developed a response unit here to generate weighted genetic feature vector to simulate the biological interaction mechanism between a specific drug and the genes. For the predictor construction level, we built this level integrating a factorization machine (FM) component with a deep neural network component. The FM can well handle the discrete chemical information and both low-order and high-order feature interactions could be sufficiently learned. Impressively, the SRDFM works well on both single-drug recommendation and synergic drug combination. Experiment result on both single-drug and synergetic drug data sets have shown the efficiency of the SRDFM. The Python implementation for the proposed SRDFM is available at at https://github.com/RanSuLab/SRDFM Contact: ran.su@tju.edu.cn, gbx@mju.edu.cn and weileyi@sdu.edu.cn.

Computational Pipeline for Rational Drug Combination Screening in Patient-Derived Cells

In many complex diseases, such as cancers, resistance to monotherapies easily occurs, and longer-term treatment responses often require combinatorial therapies as next-line regimens. However, due to a massive number of possible drug combinations to test, there is a need for systematic and rational approaches to finding safe and effective drug combinations for each individual patient. This protocol describes an ecosystem of computational methods to guide high-throughput combinatorial screening that help experimental researchers to identify optimal drug combinations in terms of synergy, efficacy, and/or selectivity for further preclinical and clinical investigation. The methods are demonstrated in the context of combinatorial screening in primary cells of leukemia patients, where the translational aim is to identify drug combinations that show not only high synergy but also maximal cancer-selectivity. The mechanism-agnostic and cost-effective computational methods are widely applicable to various cancer types, which are amenable to drug testing, as the computational methods take as input only the phenotypic measurements of a subset of drug combinations, without requiring target information or genomic profiles of the patient samples.

Systematic review of computational methods for drug combination prediction

Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.

Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer

Each patient's cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

Inhibitor Combinations Reveal Wiring of the Proteostasis Network in Prostate Cancer Cells

The protein homeostasis (proteostasis) network is composed of multiple pathways that work together to balance protein folding, stability, and turnover. Cancer cells are particularly reliant on this network; however, it is hypothesized that inhibition of one node might lead to compensation. To better understand these connections, we dosed 22Rv1 prostate cancer cells with inhibitors of four proteostasis targets (Hsp70, Hsp90, proteasome, and p97), either alone or in binary combinations, and measured the effects on cell growth. The results reveal a series of additive, synergistic, and antagonistic relationships, including strong synergy between inhibitors of p97 and the proteasome and striking antagonism between inhibitors of Hsp90 and the proteasome. Based on RNA-seq, these relationships are associated, in part, with activation of stress pathways. Together, these results suggest that cocktails of proteostasis inhibitors might be a powerful way of treating some cancers, although antagonism that blunts the efficacy of both molecules is also possible.

Data vignettes for the application of response surface models in drug combination analysis

This data set contains the data used in Twarog et al. (2021) to examine the robustness and utility of response surface models in drug combination analysis. It includes simulated experimental data for the evaluation of traditional index methods, as well as a processed library of interaction metrics evaluated on the Merck OncoPolyPharmacology Screen (O'Neil et al., 2016), the scripts used to implement those metrics on all tested combinations in that screen, and scripts to evaluate the performance of those metrics in comparison with real-world mechanistic classifications. Finally, the data set includes data from several published and unpublished drug combination experiments, and scripts which allow the analyses of those experiments to be replicated and applied to new data.

Prediction and identification of synergistic compound combinations against pancreatic cancer cells

Resistance to current therapies is common for pancreatic cancer and hence novel treatment options are urgently needed. In this work, we developed and validated a computational method to select synergistic compound combinations based on transcriptomic profiles from both the disease and compound side, combined with a pathway scoring system, which was then validated prospectively by testing 30 compounds (and their combinations) on PANC-1 cells. Some compounds selected as single agents showed lower GI50 values than the standard of care, gemcitabine. Compounds suggested as combination agents with standard therapy gemcitabine based on the best performing scoring system showed on average 2.82-5.18 times higher synergies compared to compounds that were predicted to be active as single agents. Examples of highly synergistic in vitro validated compound pairs include gemcitabine combined with Entinostat, thioridazine, loperamide, scriptaid and Saracatinib. Hence, the computational approach presented here was able to identify synergistic compound combinations against pancreatic cancer cells.

Synergistic Interactions of Cannabidiol with Chemotherapeutic Drugs in MCF7 Cells: Mode of Interaction and Proteomics Analysis of Mechanisms

Cannabidiol (CBD), a nonpsychoactive phytocannabinoid, has recently emerged as a potential cytotoxic agent in addition to its ameliorative activity in chemotherapy-associated side effects. In this work, the potential interactions of CBD with docetaxel (DOC), doxorubicin (DOX), paclitaxel (PTX), vinorelbine (VIN), and 7-ethyl-10-hydroxycamptothecin (SN-38) were explored in MCF7 breast adenocarcinoma cells using different synergy quantification models. The apoptotic profiles of MCF7 cells after the treatments were assessed via flow cytometry. The molecular mechanisms of CBD and the most promising combinations were investigated via label-free quantification proteomics. A strong synergy was observed across all synergy models at different molar ratios of CBD in combination with SN-38 and VIN. Intriguingly, synergy was observed for CBD with all chemotherapeutic drugs at a molar ratio of 636:1 in almost all synergy models. However, discording synergy trends warranted the validation of the selected combinations against different models. Enhanced apoptosis was observed for all synergistic CBD combinations compared to monotherapies or negative controls. A shotgun proteomics study highlighted 121 dysregulated proteins in CBD-treated MCF7 cells compared to the negative controls. We reported the inhibition of topoisomerase II β and α, cullin 1, V-type proton ATPase, and CDK-6 in CBD-treated MCF7 cells for the first time as additional cytotoxic mechanisms of CBD, alongside sabotaged energy production and reduced mitochondrial translation. We observed 91 significantly dysregulated proteins in MCF7 cells treated with the synergistic combination of CBD with SN-38 (CSN-38), compared to the monotherapies. Regulation of telomerase, cell cycle, topoisomerase I, EGFR1, protein metabolism, TP53 regulation of DNA repair, death receptor signalling, and RHO GTPase signalling pathways contributed to the proteome-wide synergistic molecular mechanisms of CSN-38. In conclusion, we identified significant synergistic interactions between CBD and the five important chemotherapeutic drugs and the key molecular pathways of CBD and its synergistic combination with SN-38 in MCF7 cells. Further in vivo and clinical studies are warranted to evaluate the implementation of CBD-based synergistic adjuvant therapies for breast cancer.

Suppression of Proliferation of Human Glioblastoma Cells by Combined Phosphodiesterase and Multidrug Resistance-Associated Protein 1 Inhibition

The paucity of currently available therapies for glioblastoma multiforme requires novel approaches to the treatment of this brain tumour. Disrupting cyclic nucleotide-signalling through phosphodiesterase (PDE) inhibition may be a promising way of suppressing glioblastoma growth. Here, we examined the effects of 28 PDE inhibitors, covering all the major PDE classes, on the proliferation of the human U87MG, A172 and T98G glioblastoma cells. The PDE10A inhibitors PF-2545920, PQ10 and papaverine, the PDE3/4 inhibitor trequinsin and the putative PDE5 inhibitor MY-5445 potently decreased glioblastoma cell proliferation. The synergistic suppression of glioblastoma cell proliferation was achieved by combining PF-2545920 and MY-5445. Furthermore, a co-incubation with drugs that block the activity of the multidrug resistance-associated protein 1 (MRP1) augmented these effects. In particular, a combination comprising the MRP1 inhibitor reversan, PF-2545920 and MY-5445, all at low micromolar concentrations, afforded nearly complete inhibition of glioblastoma cell growth. Thus, the potent suppression of glioblastoma cell viability may be achieved by combining MRP1 inhibitors with PDE inhibitors at a lower toxicity than that of the standard chemotherapeutic agents, thereby providing a new combination therapy for this challenging malignancy.

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

The lack of a unifying metric characterizing combinatorial drug interactions has impeded the development of combinatorial therapies. Here, the authors present MuSyC, a consensus synergy metric that overcomes several caveats associated with other, popular metrics.

The optimization of combinatorial drug therapies: Strategies and laboratorial platforms

Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.

MPI-based bioinformatic analysis and co-inhibitory therapy with mannose for oral squamous cell carcinoma

Mannose induces tumor cell apoptosis and inhibits glucose metabolism by accumulating intracellularly as mannose 6-phosphate while the drug sensitivity of tumors is negatively correlated with mannose phosphate isomerase gene (MPI) expression. In this study, we performed a first attempt to explore the relationship between the targeted gene MPI and immune infiltration and genetic and clinical characteristics of head and neck squamous carcinoma (HNSC) using computational algorithms and bioinformatic analysis, and further to verify the co-inhibition effects of mannose with genotoxicity, immune responses, and microbes dysbiosis in oral squamous cell carcinoma (OSCC) in vitro and in vivo. Our results found that patients with lower MPI expression had higher survival rate. The enhancement of MPI expression was in response to DNA damage gene, and ATM inhibitor was verified as a potential drug with a synergistic effect with mannose on HSC-3. In the HNSC, infiltrated immunocytes CD8⁺ T cell and B cell were the significantly reduced risk cells, while IL-22 and IFN-γ showed negative correlation with MPI. Finally, mannose could reverse immunophenotyping caused by antibiotics in mice, resulting in the decrease of CD8⁺ T cells and increase of myeloid-derived suppressor cells (MDSCs). In conclusion, the MPI gene showed a significant correlation with immune infiltration and genetic and clinical characteristics of HNSC. The treatment of ATM inhibitor, immune regulating cells of CD8⁺ T cells and MDSCs, and oral microbiomes in combination with mannose could exhibit co-inhibitory therapeutic effect for OSCC.

Metabolomic Identification of Anticancer Metabolites of Australian Propolis and Proteomic Elucidation of Its Synergistic Mechanisms with Doxorubicin in the MCF7 Cells

The combination of natural products with standard chemotherapeutic agents offers a promising strategy to enhance the efficacy or reduce the side effects of standard chemotherapy. Doxorubicin (DOX), a standard drug for breast cancer, has several disadvantages, including severe side effects and the development of drug resistance. Recently, we reported the potential bioactive markers of Australian propolis extract (AP-1) and their broad spectrum of pharmacological activities. In the present study, we explored the synergistic interactions between AP-1 and DOX in the MCF7 breast adenocarcinoma cells using different synergy quantitation models. Biochemometric and metabolomics-driven analysis was performed to identify the potential anticancer metabolites in AP-1. The molecular mechanisms of synergy were studied by analysing the apoptotic profile via flow cytometry, apoptotic proteome array and measuring the oxidative status of the MCF7 cells treated with the most synergistic combination. Furthermore, label-free quantification proteomics analysis was performed to decipher the underlying synergistic mechanisms. Five prenylated stilbenes were identified as the key metabolites in the most active AP-1 fraction. Strong synergy was observed when AP-1 was combined with DOX in the ratio of 100:0.29 (w/w) as validated by different synergy quantitation models implemented. AP-1 significantly enhanced the inhibitory effect of DOX against MCF7 cell proliferation in a dose-dependent manner with significant inhibition of the reactive oxygen species (p < 0.0001) compared to DOX alone. AP-1 enabled the reversal of DOX-mediated necrosis to programmed cell death, which may be advantageous to decline DOX-related side effects. AP-1 also significantly enhanced the apoptotic effect of DOX after 24 h of treatment with significant upregulation of catalase, HTRA2/Omi, FADD together with DR5 and DR4 TRAIL-mediated apoptosis (p < 0.05), contributing to the antiproliferative activity of AP-1. Significant upregulation of pro-apoptotic p27, PON2 and catalase with downregulated anti-apoptotic XIAP, HSP60 and HIF-1α, and increased antioxidant proteins (catalase and PON2) may be associated with the improved apoptosis and oxidative status of the synergistic combination-treated MCF7 cells compared to the mono treatments. Shotgun proteomics identified 21 significantly dysregulated proteins in the synergistic combination-treated cells versus the mono treatments. These proteins were involved in the TP53/ATM-regulated non-homologous end-joining pathway and double-strand breaks repairs, recruiting the overexpressed BRCA1 and suppressed RIF1 encoded proteins. The overexpression of UPF2 was noticed in the synergistic combination treatment, which could assist in overcoming doxorubicin resistance-associated long non-coding RNA and metastasis of the MCF7 cells. In conclusion, we identified the significant synergy and highlighted the key molecular pathways in the interaction between AP-1 and DOX in the MCF7 cells together with the AP-1 anticancer metabolites. Further in vivo and clinical studies are warranted on this synergistic combination.

Mathematical Modelling of the Molecular Mechanisms of Interaction of Tenofovir with Emtricitabine against HIV

The combination of the two nucleoside reverse transcriptase inhibitors (NRTI) tenofovir disoproxil fumarate (TDF) and emtricitabine (FTC) is used in most highly active antiretroviral therapies for treatment of HIV-1 infection, as well as in pre-exposure prophylaxis against HIV acquisition. Administered as prodrugs, these drugs are taken up by HIV-infected target cells, undergo intracellular phosphorylation and compete with natural deoxynucleoside triphosphates (dNTP) for incorporation into nascent viral DNA during reverse transcription. Once incorporated, they halt reverse transcription. In vitro studies have proposed that TDF and FTC act synergistically within an HIV-infected cell. However, it is unclear whether, and which, direct drug–drug interactions mediate the apparent synergy. The goal of this work was to refine a mechanistic model for the molecular mechanism of action (MMOA) of nucleoside analogues in order to analyse whether putative direct interactions may account for the in vitro observed synergistic effects. Our analysis suggests that depletion of dNTP pools can explain apparent synergy between TDF and FTC in HIV-infected cells at clinically relevant concentrations. Dead-end complex (DEC) formation does not seem to significantly contribute to the synergistic effect. However, in the presence of non-nucleoside reverse transcriptase inhibitors (NNRTIs), its role might be more relevant, as previously reported in experimental in vitro studies.

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

Using response surface models to analyze drug combinations

Quantitative evaluation of how drugs combine to elicit a biological response is crucial for drug development. Evaluations of drug combinations are often performed using index-based methods, which are known to be biased and unstable. We examine how these methods can produce misleadingly structured patterns of bias, leading to erroneous judgments of synergy or antagonism. By contrast, response surface models are less prone to these defects and can be applied to a wide range of data that have appeared in recent literature, including the measurement of combination therapeutic windows and the analysis of discrete experimental measures, three-way drug combinations, and atypical response behaviors.

Drug combination therapy for emerging viral diseases

Effective therapeutics to combat emerging viral infections are an unmet need. Historically, treatments for chronic viral infections with single drugs have not been successful, as exemplified by human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infections. Combination therapy for these diseases has led to improved clinical outcomes with dramatic reductions in viral load, morbidity, and mortality. Drug combinations can enhance therapeutic efficacy through additive, and ideally synergistic, effects for emerging and re-emerging viruses, such as influenza, severe acute respiratory syndrome-coronavirus (SARS-CoV), Middle East respiratory syndrome (MERS)-CoV, Ebola, Zika, and SARS-coronavirus 2 (CoV-2). Although novel drug development through traditional pipelines remains a priority, in the interim, effective synergistic drug candidates could be rapidly identified by drug-repurposing screens, facilitating accelerated paths to clinical testing and potential emergency use authorizations.

Synergistic Inhibition of SARS-CoV-2 Replication Using Disulfiram/Ebselen and Remdesivir

The SARS-CoV-2 replication and transcription complex (RTC) comprising nonstructural protein (nsp) 2-16 plays crucial roles in viral replication, reducing the efficacy of broad-spectrum nucleoside analog drugs such as remdesivir and evading innate immune responses. Most studies target a specific viral component of the RTC such as the main protease or the RNA-dependent RNA polymerase. In contrast, our strategy is to target multiple conserved domains of the RTC to prevent SARS-CoV-2 genome replication and to create a high barrier to viral resistance and/or evasion of antiviral drugs. We show that the clinically safe Zn-ejector drugs disulfiram and ebselen can target conserved Zn2+ sites in SARS-CoV-2 nsp13 and nsp14 and inhibit nsp13 ATPase and nsp14 exoribonuclease activities. As the SARS-CoV-2 nsp14 domain targeted by disulfiram/ebselen is involved in RNA fidelity control, our strategy allows coupling of the Zn-ejector drug with a broad-spectrum nucleoside analog that would otherwise be excised by the nsp14 proofreading domain. As proof-of-concept, we show that disulfiram/ebselen, when combined with remdesivir, can synergistically inhibit SARS-CoV-2 replication in Vero E6 cells. We present a mechanism of action and the advantages of our multitargeting strategy, which can be applied to any type of coronavirus with conserved Zn2+ sites.

Complex interactions of lovastatin with 10 chemotherapeutic drugs: a rigorous evaluation of synergism and antagonism

Background

Evidence bearing on the role of statins in the prevention and treatment of cancer is confounded by the diversity of statins, chemotherapeutic agents and cancer types included in the numerous published studies; consequently, the adjunctive value of statins with chemotherapy remains uncertain.

Methods

We assayed lovastatin in combination with each of ten commonly prescribed chemotherapy drugs in highly reproducible in vitro assays, using a neutral cellular substrate, Saccharomyces cerevisiae. Cell density (OD₆₀₀) data were analyzed for synergism and antagonism using the Loewe additivity model implemented with the Combenefit software.

Results

Four of the ten chemotherapy drugs – tamoxifen, doxorubicin, methotrexate and rapamycin – exhibited net synergism with lovastatin. The remaining six agents (5-fluorouracil, gemcitabine, epothilone, cisplatin, cyclophosphamide and etoposide) compiled neutral or antagonistic scores. Distinctive patterns of synergism and antagonism, often coexisting within the same concentration space, were documented with the various combinations, including those with net synergism scores. Two drug pairs, lovastatin combined with tamoxifen or cisplatin, were also assayed in human cell lines as proof of principle.

Conclusions

The synergistic interactions of tamoxifen, doxorubicin, methotrexate and rapamycin with lovastatin – because they suggest the possibility of clinical utility - merit further exploration and validation in cell lines and animal models. No less importantly, strong antagonistic interactions between certain agents and lovastatin argue for a cautious, data-driven approach before adding a statin to any chemotherapeutic regimen. We also urge awareness of adventitious statin usage by patients entering cancer treatment protocols.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07963-w.

Re-defining synthetic lethality by phenotypic profiling for precision oncology

High-throughput functional and genomic screening techniques provide systematic means for phenotypic discovery. Using synthetic lethality (SL) as a paradigm for anticancer drug and target discovery, we describe how these screening technologies may offer new possibilities to identify therapeutically relevant and selective SL interactions by addressing some of the challenges that have made robust discovery of SL candidates difficult. We further introduce an extended concept of SL interaction, in which a simultaneous perturbation of two or more cellular components reduces cell viability more than expected by their individual effects, which we feel is highly befitting for anticancer applications. We also highlight the potential benefits and challenges related to computational quantification of synergistic interactions and cancer selectivity. Finally, we explore how tumoral heterogeneity can be exploited to find phenotype-specific SL interactions for precision oncology using high-throughput functional screening and the exciting opportunities these methods provide for the identification of subclonal SL interactions.

Combination of Ginsenosides Rb2 and Rg3 Promotes Angiogenic Phenotype of Human Endothelial Cells via PI3K/Akt and MAPK/ERK Pathways

Shexiang Baoxin Pill (SBP) is an oral formulation of Chinese materia medica for the treatment of angina pectoris. It displays pleiotropic roles in protecting the cardiovascular system. However, the mode of action of SBP in promoting angiogenesis, and in particular the synergy between its constituents is currently not fully understood. The combination of ginsenosides Rb2 and Rg3 were studied in human umbilical vein endothelial cells (HUVECs) for their proangiogenic effects. To understand the mode of action of the combination in more mechanistic detail, RNA-Seq analysis was conducted, and differentially expressed genes (DEGs), pathway analysis and Weighted Gene Correlation Network Analysis (WGCNA) were applied to further identify important genes that a play pivotal role in the combination treatment. The effects of pathway-specific inhibitors were observed to provide further support for the hypothesized mode of action of the combination. Ginsenosides Rb2 and Rg3 synergistically promoted HUVEC proliferation and tube formation under defined culture conditions. Also, the combination of Rb2/Rg3 rescued cells from homocysteine-induced damage. mRNA expression of CXCL8, CYR61, FGF16 and FGFRL1 was significantly elevated by the Rb2/Rg3 treatment, and representative signaling pathways induced by these genes were found. The increase of protein levels of phosphorylated-Akt and ERK42/44 by the Rb2/Rg3 combination supports the notion that it promotes endothelial cell proliferation via the PI3K/Akt and MAPK/ERK signaling pathways. The present study provides the hypothesis that SBP, via ginsenosides Rb2 and Rg3, involves the CXCR1/2 CXCL8 (IL8)-mediated PI3K/Akt and MAPK/ERK signaling pathways in achieving its proangiogenic effects.

Drug synergy scoring using minimal dose response matrices

Objective

Combinations of pharmacological agents are essential for disease control and prevention, offering many advantages over monotherapies, with one of these being drug synergy. The state-of-the-art method to profile drug synergy in preclinical research is by using dose–response matrices in disease-appropriate models, however this approach is frequently labour intensive and cost-ineffective, particularly when performed in a medium- to high-throughput fashion. Thus, in this study, we set out to optimise a parameter of this methodology, determining the minimal matrix size that can be used to robustly detect and quantify synergy between two drugs.

Results

We used a drug matrix reduction workflow that allowed the identification of a minimal drug matrix capable of robustly detecting and quantifying drug synergy. These minimal matrices utilise substantially less reagents and data processing power than their typically used larger counterparts. Focusing on the antileukemic efficacy of the chemotherapy combination of cytarabine and inhibitors of ribonucleotide reductase, we could show that detection and quantification of drug synergy by three common synergy models was well-tolerated despite reducing matrix size from 8 × 8 to 4 × 4. Overall, the optimisation of drug synergy scoring as presented here could inform future medium- to high-throughput drug synergy screening strategies in pre-clinical research.

Multiobjective optimization identifies cancer-selective combination therapies

Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.

Divergent Synthesis of Methylisatoid and Tryptanthrin Derivatives by Ph3P-I2-Mediated Reaction of Isatins with and without Alcohols

A novel phosphonium-mediated reaction of isatins is described. In the presence of alcohol, the reaction proceeds to furnish C-12 modified tryptanthrin derivatives. Without alcohol, self-dimerization of isatins gives rise to tryptanthrin and its analogs. This divergent and step-economic approach provides a facile access to diverse indoloquinazoline structures including the natural alkaloids, methylisatoid and cephalanthrin B, in high yields from simple precursors under mild and metal-free reaction conditions.

A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects

We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through higher-order tensors, and efficiently learns latent factors of the tensor using powerful factorization machines. The approach enables comboFM to leverage information from previous experiments performed on similar drugs and cells when predicting responses of new combinations in so far untested cells; thereby, it achieves highly accurate predictions despite sparsely populated data tensors. We demonstrate high predictive performance of comboFM in various prediction scenarios using data from cancer cell line pharmacogenomic screens. Subsequent experimental validation of a set of previously untested drug combinations further supports the practical and robust applicability of comboFM. For instance, we confirm a novel synergy between anaplastic lymphoma kinase (ALK) inhibitor crizotinib and proteasome inhibitor bortezomib in lymphoma cells. Overall, our results demonstrate that comboFM provides an effective means for systematic pre-screening of drug combinations to support precision oncology applications.

Combinatorial treatments have become a standard of care for various complex diseases including cancers. Here, the authors show that combinatorial responses of two anticancer drugs can be accurately predicted using factorization machines trained on large-scale pharmacogenomic data for guiding precision oncology studies.