Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

Motivation

While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies.

Results

DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations.

Availability and implementation

DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy.

Contact

klambauer@bioinf.jku.at.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genome-wide mutant profiling predicts the mechanism of a Lipid II binding antibiotic

Identifying targets of antibacterial compounds remains a challenging step in antibiotic development. We have developed a two-pronged functional genomics approach to predict mechanism of action that uses mutant fitness data from antibiotic-treated transposon libraries containing both upregulation and inactivation mutants. We treated a Staphylococcus aureus transposon library containing 690,000 unique insertions with 32 antibiotics. Upregulation signatures, identified from directional biases in insertions, revealed known molecular targets and resistance mechanisms for the majority of these. Because single gene upregulation does not always confer resistance, we used a complementary machine learning approach to predict mechanism from inactivation mutant fitness profiles. This approach suggested the cell wall precursor Lipid II as the molecular target of the lysocins, a mechanism we have confirmed. We conclude that docking to membrane-anchored Lipid II precedes the selective bacteriolysis that distinguishes these lytic natural products, showing the utility of our approach for nominating antibiotic mechanism of action.

Antifungal Compounds against Candida Infections from Traditional Chinese Medicine

Infections caused by Candida albicans, often refractory and with high morbidity and mortality, cause a heavy burden on the public health while the current antifungal drugs are limited and are associated with toxicity and resistance. Many plant-derived molecules including compounds isolated from traditional Chinese medicine (TCM) are reported to have antifungal activity through different targets such as cell membrane, cell wall, mitochondria, and virulence factors. Here, we review the recent progress in the anti-Candida compounds from TCM, as well as their antifungal mechanisms. Considering the diverse targets and structures, compounds from TCM might be a potential library for antifungal drug development.

Emerging and evolving concepts in gene essentiality

Gene essentiality is a founding concept of genetics with important implications in both fundamental and applied research. Multiple screens have been performed over the years in bacteria, yeasts, animals and more recently in human cells to identify essential genes. A mounting body of evidence suggests that gene essentiality, rather than being a static and binary property, is both context dependent and evolvable in all kingdoms of life. This concept of a non-absolute nature of gene essentiality changes our fundamental understanding of essential biological processes and could directly affect future treatment strategies for cancer and infectious diseases.

Antibiotic efficacy-context matters

Antibiotic lethality is a complex physiological process, sensitive to external cues. Recent advances using systems approaches have revealed how events downstream of primary target inhibition actively participate in antibiotic death processes. In particular, altered metabolism, translational stress and DNA damage each contribute to antibiotic-induced cell death. Moreover, environmental factors such as oxygen availability, extracellular metabolites, population heterogeneity and multidrug contexts alter antibiotic efficacy by impacting bacterial metabolism and stress responses. Here we review recent studies on antibiotic efficacy and highlight insights gained on the involvement of cellular respiration, redox stress and altered metabolism in antibiotic lethality. We discuss the complexity found in natural environments and highlight knowledge gaps in antibiotic lethality that may be addressed using systems approaches.

Phytotherapy as an alternative to conventional antimicrobials: combating microbial resistance

INTRODUCTION

In the modern antimicrobial era, the rapid spread of resistance to antibiotics and introduction of new and mutating viruses is a global concern. Combating antimicrobial resistant microbes (AMR) requires coordinated international efforts that incorporate new conventional antibiotic development as well as development of alternative drugs with antimicrobial activity, management of existing antimicrobials, and rapid detection of AMR pathogens. Areas covered: This manuscript discusses some conventional strategies to control microbial resistance. The main purpose of the manuscript is to present information on specific herbal medicines that may serve as good treatment alternatives to conventional antimicrobials for infections sensitive to conventional as well as resistant strains of microorganisms. Expert commentary: Identification of potential new antimicrobials is challenging; however, one source for potential structurally diverse and complex antimicrobials are natural products. Natural products may have advantages over other post-germ theory antimicrobials. Many antimicrobial herbal medicines possess simultaneous antibacterial, antifungal, antiprotozoal and/or antiviral properties. Herbal products have the potential to boost host resistance to infections, particularly in immunocompromised patients. Antimicrobial broad-spectrum activity in conjunction with immunostimulatory properties may help to prevent microbial resistance to herbal medicine. As part of the efforts to broaden use of herbal medicines to treat microbial infections, pre-clinical and clinical testing guidelines of these compounds as a whole should be implemented to ensure consistency in formulation, efficacy and safety.

Chemical genetics in drug discovery

Chemical-genetic approaches are based on measuring the cellular outcome of combining genetic and chemical perturbations in large-numbers in tandem. In these approaches the contribution of every gene to the fitness of an organism is measured upon exposure to different chemicals. Current technological advances enable the application of chemical genetics to almost any organism and at an unprecedented throughput. Here we review the underlying concepts behind chemical genetics, present its different vignettes and illustrate how such approaches can propel drug discovery.

High-throughput identification and rational design of synergistic small-molecule pairs for combating and bypassing antibiotic resistance

Antibiotic-resistant infections kill approximately 23,000 people and cost $20,000,000,000 each year in the United States alone despite the widespread use of small-molecule antimicrobial combination therapy. Antibiotic combinations typically have an additive effect: the efficacy of the combination matches the sum of the efficacies of each antibiotic when used alone. Small molecules can also act synergistically when the efficacy of the combination is greater than the additive efficacy. However, synergistic combinations are rare and have been historically difficult to identify. High-throughput identification of synergistic pairs is limited by the scale of potential combinations: a modest collection of 1,000 small molecules involves 1 million pairwise combinations. Here, we describe a high-throughput method for rapid identification of synergistic small-molecule pairs, the overlap2 method (O2M). O2M extracts patterns from chemical-genetic datasets, which are created when a collection of mutants is grown in the presence of hundreds of different small molecules, producing a precise set of phenotypes induced by each small molecule across the mutant set. The identification of mutants that show the same phenotype when treated with known synergistic molecules allows us to pinpoint additional molecule combinations that also act synergistically. As a proof of concept, we focus on combinations with the antibiotics trimethoprim and sulfamethizole, which had been standard treatment against urinary tract infections until widespread resistance decreased efficacy. Using O2M, we screened a library of 2,000 small molecules and identified several that synergize with the antibiotic trimethoprim and/or sulfamethizole. The most potent of these synergistic interactions is with the antiviral drug azidothymidine (AZT). We then demonstrate that understanding the molecular mechanism underlying small-molecule synergistic interactions allows the rational design of additional combinations that bypass drug resistance. Trimethoprim and sulfamethizole are both folate biosynthesis inhibitors. We find that this activity disrupts nucleotide homeostasis, which blocks DNA replication in the presence of AZT. Building on these data, we show that other small molecules that disrupt nucleotide homeostasis through other mechanisms (hydroxyurea and floxuridine) also act synergistically with AZT. These novel combinations inhibit the growth and virulence of trimethoprim-resistant clinical Escherichia coli and Klebsiella pneumoniae isolates, suggesting that they may be able to be rapidly advanced into clinical use. In sum, we present a generalizable method to screen for novel synergistic combinations, to identify particular mechanisms resulting in synergy, and to use the mechanistic knowledge to rationally design new combinations that bypass drug resistance.

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.

Combinatorial Drug Screening Identifies Ewing Sarcoma-specific Sensitivities

Improvements in survival for Ewing sarcoma pediatric and adolescent patients have been modest over the past 20 years. Combinations of anticancer agents endure as an option to overcome resistance to single treatments caused by compensatory pathways. Moreover, combinations are thought to lessen any associated adverse side effects through reduced dosing, which is particularly important in childhood tumors. Using a parallel phenotypic combinatorial screening approach of cells derived from three pediatric tumor types, we identified Ewing sarcoma-specific interactions of a diverse set of targeted agents including approved drugs. We were able to retrieve highly synergistic drug combinations specific for Ewing sarcoma and identified signaling processes important for Ewing sarcoma cell proliferation determined by EWS-FLI1 We generated a molecular target profile of PKC412, a multikinase inhibitor with strong synergistic propensity in Ewing sarcoma, revealing its targets in critical Ewing sarcoma signaling routes. Using a multilevel experimental approach including quantitative phosphoproteomics, we analyzed the molecular rationale behind the disease-specific synergistic effect of simultaneous application of PKC412 and IGF1R inhibitors. The mechanism of the drug synergy between these inhibitors is different from the sum of the mechanisms of the single agents. The combination effectively inhibited pathway crosstalk and averted feedback loop repression, in EWS-FLI1-dependent manner. Mol Cancer Ther; 16(1); 88-101. ©2016 AACR.

Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism

The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery.

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

Neglected disease drug discovery is generally poorly funded compared with major diseases and hence there is an increasing focus on collaboration and precompetitive efforts such as public-private partnerships (PPPs). The More Medicines for Tuberculosis (MM4TB) project is one such collaboration funded by the EU with the goal of discovering new drugs for tuberculosis. Collaborative Drug Discovery has provided a commercial web-based platform called CDD Vault which is a hosted collaborative solution for securely sharing diverse chemistry and biology data. Using CDD Vault alongside other commercial and free cheminformatics tools has enabled support of this and other large collaborative projects, aiding drug discovery efforts and fostering collaboration. We will describe CDD's efforts in assisting with the MM4TB project.

Antifungal Drugs: The Current Armamentarium and Development of New Agents

Invasive fungal infections are becoming an increasingly important cause of human mortality and morbidity, particularly for immunocompromised populations. The fungal pathogens Candida albicans , Cryptococcus neoformans , and Aspergillus fumigatus collectively contribute to over 1 million human deaths annually. Hence, the importance of safe and effective antifungal therapeutics for the practice of modern medicine has never been greater. Given that fungi are eukaryotes like their human host, the number of unique molecular targets that can be exploited for drug development remains limited. Only three classes of molecules are currently approved for the treatment of invasive mycoses. The efficacy of these agents is compromised by host toxicity, fungistatic activity, or the emergence of drug resistance in pathogen populations. Here we describe our current arsenal of antifungals and highlight current strategies that are being employed to improve the therapeutic safety and efficacy of these drugs. We discuss state-of-the-art approaches to discover novel chemical matter with antifungal activity and highlight some of the most promising new targets for antifungal drug development. We feature the benefits of combination therapy as a strategy to expand our current repertoire of antifungals and discuss the antifungal combinations that have shown the greatest potential for clinical development. Despite the paucity of new classes of antifungals that have come to market in recent years, it is clear that by leveraging innovative approaches to drug discovery and cultivating collaborations between academia and industry, there is great potential to bolster the antifungal armamentarium.

Discovery of Ibomycin, a Complex Macrolactone that Exerts Antifungal Activity by Impeding Endocytic Trafficking and Membrane Function

Natural products are invaluable historic sources of drugs for infectious diseases; however, the discovery of novel antimicrobial chemical scaffolds has waned in recent years. Concurrently, there is a pressing need for improved therapeutics to treat fungal infections. We employed a co-culture screen to identify ibomycin, a large polyketide macrolactone that has preferential killing activity against Cryptococcus neoformans. Using chemical and genome methods, we determined the structure of ibomycin and identified the biosynthetic cluster responsible for its synthesis. Chemogenomic profiling coupled with cell biological assays link ibomycin bioactivity to membrane function. The preferential activity of ibomycin toward C. neoformans is due to the ability of the compound to selectively permeate its cell wall. These results delineate a novel antifungal agent that is produced by one of the largest documented biosynthetic clusters to date and underscore the fact that there remains significant untapped chemical diversity of natural products with application in antimicrobial research.

Combinatorial strategies for combating invasive fungal infections

Invasive fungal infections are an important cause of human mortality and morbidity, particularly for immunocompromised populations. However, there remains a paucity of antifungal drug treatments available to combat these fungal pathogens. Further, antifungal compounds are plagued with problems such as host toxicity, fungistatic activity, and the emergence of drug resistance in pathogen populations. A promising therapeutic strategy to increase drug effectiveness and mitigate the emergence of drug resistance is through the use of combination drug therapy. In this review we describe the current arsenal of antifungals in medicine and elaborate on the benefits of combination therapy to expand our current antifungal drug repertoire. We examine those antifungal combinations that have shown potential against fungal pathogens and discuss strategies being employed to discover novel combination therapeutics, in particular combining antifungal agents with non-antifungal bioactive compounds. The findings summarized in this review highlight the promise of combinatorial strategies in combatting invasive mycoses.

Chemogenomics and orthology-based design of antibiotic combination therapies

Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms.

Shedding Light on Synergistic Chemical Genetic Connections with Machine Learning

Machine learning can be used to predict compounds acting synergistically, and this could greatly expand the universe of available potential treatments for diseases that are currently hidden in the dark chemical matter.