Chemogenomics and orthology-based design of antibiotic combination therapies

The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution

In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs-those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non-interacting drugs and implement the two most common models for drug independence-Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a 'fair' comparison. Yet, exceptions exist for drugs with steep dose-response curves. Combining a bacteriostatic and a bactericidal drug which can kill non-replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio-even if not always optimal-is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single-cell level.

Analysis of Co-localized Biosynthetic Gene Clusters Identifies a Membrane-Permeabilizing Natural Product

Combination therapy is an effective strategy to combat antibiotic resistance. Multiple synergistic antimicrobial combinations are produced by enzymes encoded in biosynthetic gene clusters (BGCs) that co-localize on the bacterial genome. This phenomenon led to the hypothesis that mining co-localized BGCs will reveal new synergistic combinations of natural products. Here, we bioinformatically identified 38 pairs of co-localized BGCs, which we predict to produce natural products that are related to known compounds, including polycyclic tetramate macrolactams (PoTeMs). We further showed that ikarugamycin, a PoTeM, increases the membrane permeability of Acinetobacter baumannii and Staphylococcus aureus, which suggests that ikarugamycin might be an adjuvant that facilitates the entry of other natural products. Our work outlines a promising avenue to discover synergistic combinations of natural products by mining bacterial genomes.

Morpholinium-based Ionic Liquids as Potent Antibiofilm and Sensitizing Agents for the Control of Pseudomonas aeruginosa

Rising antimicrobial resistance is a critical threat to worldwide public health. To address the increasing antibiotic tolerance, diverse antimicrobial agents are examined for their ability to decrease bacterial resistance. One of the most relevant and persistent human pathogens is Pseudomonas aeruginosa. Our study investigates the anti-biofilm and sensitizing activity of 12 morpholinium-based ionic liquids with herbicidal anions on four clinically relevant P. aeruginosa strains. Among all tested compounds, four ionic liquids prevented biofilm formation at sub-minimum inhibitory concentrations for all investigated strains. For the first time, we established a hormetic effect on biofilm formation for P. aeruginosa strains subjected to an ionic liquid treatment. Interestingly, while ionic liquids with 4,4-didecylmorpholinium [Dec2Mor]+ are more efficient against planktonic bacteria, 4-decyl-4-ethylmorpholinium [DecEtMor]+ showed more potent inhibition of biofilm formation. Ionic liquids with 4,4-didecylmorpholinium ([Dec2Mor]+) cations even induced biofilm formation by strain 39016 at high concentrations due to flocculation. Morpholinium-based ionic liquids were also shown to enhance the efficacy of commonly used antibiotics from different chemical groups. We demonstrate that this synergy is associated with the mode of action of the antibiotics.

Transfer learning predicts species-specific drug interactions in emerging pathogens

Machine learning (ML) algorithms are necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However, existing ML approaches cannot be applied to emerging and under-studied pathogens with limited training data. To address this, we developed a transfer learning and crowdsourcing framework (TACTIC) to train ML models on data from multiple bacteria. TACTIC was built using 2,965 drug interactions from 12 bacterial strains and outperformed traditional ML models in predicting drug interaction outcomes for species that lack training data. Top TACTIC model features revealed genetic and metabolic factors that influence cross-species and species-specific drug interaction outcomes. Upon analyzing ~600,000 predicted drug interactions across 9 metabolic environments and 18 bacterial strains, we identified a small set of drug interactions that are selectively synergistic against Gram-negative (e.g., A. baumannii) and non-tuberculous mycobacteria (NTM) pathogens. We experimentally validated synergistic drug combinations containing clarithromycin, ampicillin, and mecillinam against M. abscessus, an emerging pathogen with growing levels of antibiotic resistance. Lastly, we leveraged TACTIC to propose selectively synergistic drug combinations to treat bacterial eye infections (endophthalmitis).

Automated and miniaturized screening of antibiotic combinations via robotic-printed combinatorial droplet platform

Antimicrobial resistance (AMR) has become a global health crisis in need of novel solutions. To this end, antibiotic combination therapies, which combine multiple antibiotics for treatment, have attracted significant attention as a potential approach for combating AMR. To facilitate advances in antibiotic combination therapies, most notably in investigating antibiotic interactions and identifying synergistic antibiotic combinations however, there remains a need for automated high-throughput platforms that can create and examine antibiotic combinations on-demand, at scale, and with minimal reagent consumption. To address these challenges, we have developed a Robotic-Printed Combinatorial Droplet (RoboDrop) platform by integrating a programmable droplet microfluidic device that generates antibiotic combinations in nanoliter droplets in automation, a robotic arm that arranges the droplets in an array, and a camera that images the array of thousands of droplets in parallel. We further implement a resazurin-based bacterial viability assay to accelerate our antibiotic combination testing. As a demonstration, we use RoboDrop to corroborate two pairs of antibiotics with known interactions and subsequently identify a new synergistic combination of cefsulodin, penicillin, and oxacillin against a model E. coli strain. We therefore envision RoboDrop becoming a useful tool to efficiently identify new synergistic antibiotic combinations toward combating AMR.

Critical role of growth medium for detecting drug interactions in Gram-negative bacteria that model in vivo responses

The rise in infections caused by multidrug-resistant (MDR) bacteria has necessitated a variety of clinical approaches, including the use of antibiotic combinations. Here, we tested the hypothesis that drug-drug interactions vary in different media, and determined which in vitro models best predict drug interactions in the lungs. We systematically studied pair-wise antibiotic interactions in three different media, CAMHB, (a rich lab medium standard for antibiotic susceptibility testing), a urine mimetic medium (UMM), and a minimal medium of M9 salts supplemented with glucose and iron (M9Glu) with three Gram-negative ESKAPE pathogens, Acinetobacter baumannii (Ab), Klebsiella pneumoniae (Kp), and Pseudomonas aeruginosa (Pa). There were pronounced differences in responses to antibiotic combinations between the three bacterial species grown in the same medium. However, within species, PaO1 responded to drug combinations similarly when grown in all three different media, whereas Ab17978 and other Ab clinical isolates responded similarly when grown in CAMHB and M9Glu medium. By contrast, drug interactions in Kp43816, and other Kp clinical isolates poorly correlated across different media. To assess whether any of these media were predictive of antibiotic interactions against Kp in the lungs of mice, we tested three antibiotic combination pairs. In vitro measurements in M9Glu, but not rich medium or UMM, predicted in vivo outcomes. This work demonstrates that antibiotic interactions are highly variable across three Gram-negative pathogens and highlights the importance of growth medium by showing a superior correlation between in vitro interactions in a minimal growth medium and in vivo outcomes.

IMPORTANCE

Drug-resistant bacterial infections are a growing concern and have only continued to increase during the SARS-CoV-2 pandemic. Though not routinely used for Gram-negative bacteria, drug combinations are sometimes used for serious infections and may become more widely used as the prevalence of extremely drug-resistant organisms increases. To date, reliable methods are not available for identifying beneficial drug combinations for a particular infection. Our study shows variability across strains in how drug interactions are impacted by growth conditions. It also demonstrates that testing drug combinations in tissue-relevant growth conditions for some strains better models what happens during infection and may better inform combination therapy selection.

CAD204520 Targets NOTCH1 PEST Domain Mutations in Lymphoproliferative Disorders

NOTCH1 PEST domain mutations are often seen in hematopoietic malignancies, including T-cell acute lymphoblastic leukemia (T-ALL), chronic lymphocytic leukemia (CLL), splenic marginal zone lymphoma (SMZL), mantle cell lymphoma (MCL), and diffuse large B-cell lymphoma (DLBCL). These mutations play a key role in the development and progression of lymphoproliferative tumors by increasing the Notch signaling and, consequently, promoting cell proliferation, survival, migration, and suppressing apoptosis. There is currently no specific treatment available for cancers caused by NOTCH1 PEST domain mutations. However, several NOTCH1 inhibitors are in development. Among these, inhibition of the Sarco-endoplasmic Ca2+-ATPase (SERCA) showed a greater effect in NOTCH1-mutated tumors compared to the wild-type ones. One example is CAD204520, a benzimidazole derivative active in T-ALL cells harboring NOTCH1 mutations. In this study, we preclinically assessed the effect of CAD204520 in CLL and MCL models and showed that NOTCH1 PEST domain mutations sensitize cells to the anti-leukemic activity mediated by CAD204520. Additionally, we tested the potential of CAD204520 in combination with the current first-line treatment of CLL, venetoclax, and ibrutinib. CAD204520 enhanced the synergistic effect of this treatment regimen only in samples harboring the NOTCH1 PEST domain mutations, thus supporting a role for Notch inhibition in these tumors. In summary, our work provides strong support for the development of CAD204520 as a novel therapeutic approach also in chronic lymphoproliferative disorders carrying NOTCH1 PEST domain mutations, emerging as a promising molecule for combination treatment in this aggressive subset of patients.

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.

Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling

Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.

Identification of Robust Antibiotic Subgroups by Integrating Multi-Species Drug-Drug Interactions

Previous studies have shown that antibiotics can be divided into groups, and drug-drug interactions (DDI) depend on their groups. However, these studies focused on a specific bacteria strain (i.e., Escherichia coli BW25113). Existing datasets often contain noise. Noisy labeled data may have a bad effect on the clustering results. To address this problem, we developed a multi-source information fusion method for integrating DDI information from multiple bacterial strains. Specifically, we calculated drug similarities based on the DDI network of each bacterial strain and then fused these drug similarity matrices to obtain a new fused similarity matrix. The fused similarity matrix was combined with the T-distributed stochastic neighbor embedding algorithm, and hierarchical clustering algorithm can effectively identify antibiotic subgroups. These antibiotic subgroups are strongly correlated with known antibiotic classifications, and group-group interactions are almost monochromatic. In summary, our method provides a promising framework for understanding the mechanism of action of antibiotics and exploring multi-species group-group interactions.

Integrating multi-source drug information to cluster drug-drug interaction network

Characterizing drug-drug interactions is important to improve efficacy and/or slow down the evolution of antimicrobial resistance. Experimental methods are both time-consuming and laborious for characterizing drug-drug interactions. In recent years, many computational methods have been proposed to explore drug-drug interactions. However, these methods failed to effectively integrate multi-source drug information. In this study, we propose a similarity matrix fusion (SMF) method to integrate four drug information (i.e., structural similarity, pharmaceutical similarity, phenotypic similarity and therapeutic similarity). SMF combined with t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering algorithm can effectively identify drug groups and group-group interactions are almost monochromatic (purely synergetic or purely antagonistic). To evaluate clustering quality (i.e., monochromaticity), two measures (edge purity and edge normalized mutual information) are proposed, and SMF showed the best performance. In addition, clustered drug-drug interaction network can also be used to predict new drug-drug interactions (accuracy = 0.741). Overall, SMF provides a comprehensive view to understand drug groups and group-group interactions.

Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens

Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process.

Tools to develop antibiotic combinations that target drug tolerance in Mycobacterium tuberculosis

Combination therapy is necessary to treat tuberculosis to decrease the rate of disease relapse and prevent the acquisition of drug resistance, and shorter regimens are urgently needed. The adaptation of Mycobacterium tuberculosis to various lesion microenvironments in infection induces various states of slow replication and non-replication and subsequent antibiotic tolerance. This non-heritable tolerance to treatment necessitates lengthy combination therapy. Therefore, it is critical to develop combination therapies that specifically target the different types of drug-tolerant cells in infection. As new tools to study drug combinations earlier in the drug development pipeline are being actively developed, we must consider how to best model the drug-tolerant cells to use these tools to design the best antibiotic combinations that target those cells and shorten tuberculosis therapy. In this review, we discuss the factors underlying types of drug tolerance, how combination therapy targets these populations of bacteria, and how drug tolerance is currently modeled for the development of tuberculosis multidrug therapy. We highlight areas for future studies to develop new tools that better model drug tolerance in tuberculosis infection specifically for combination therapy testing to bring the best drug regimens forward to the clinic.

Antibiotic discovery in the artificial intelligence era

As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.

Exploring the Antimicrobial and Pharmacological Potential of NF22 as a Potent Inhibitor of E. coli DNA Gyrase: An In Vitro and In Silico Study

Toward the search for novel antimicrobial agents to control pathogenic E. coli-associated infections, a series of novel norfloxacin derivatives were screened for antimicrobial activities. The norfloxacin derivative, 1-ethyl-6-fluoro-7-(4-(2-(2-(3-hydroxybenzylidene)hydrazinyl)-2-oxoethyl)piperazin-1-yl)-4-oxo-1,4-dihydroquinoline-3-carboxylic acid (NF22) demonstrated excellent antibacterial activities against E. coli ATCC 25922 (MIC = 0.0625 μg/mL) and MDR E. coli 1-3 (MIC = 1, 2 and 1 µg/mL). The time-kill kinetic studies have demonstrated that the NF22 was advantageous over norfloxacin and ciprofloxacin in killing the control and MDR E. coli strains. The checkerboard assay showed that NF22 in combination with tetracycline had a synergistic effect against the E. coli strains. The experimental findings are supported by molecular modeling studies on DNA gyrase, explaining the interactions involved for compound NF22, compared to norfloxacin and ciprofloxacin. Further, the compound was also evaluated for various pharmacokinetics (absorption, metabolism, distribution, toxicity and excretion) as well as drug-likeness properties. Our data have highlighted the potential of norfloxacin by restoring its efficacy against E. coli which could lead to the development of new antimicrobial agents.

Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements

A challenge in tuberculosis treatment regimen design is the necessity to combine three or more antibiotics. We narrow the prohibitively large search space by breaking down high-order drug combinations into drug pair units. Using pairwise in vitro measurements, we train machine learning models to predict higher-order combination treatment outcomes in the relapsing BALB/c mouse model. Classifiers perform well and predict many of the >500 possible combinations among 12 antibiotics to be improved over bedaquiline + pretomanid + linezolid, a treatment-shortening regimen compared with the standard of care in mice. We reformulate classifiers as simple rulesets to reveal guiding principles of constructing combination therapies for both preclinical and clinical outcomes. One example ruleset combines a drug pair that is synergistic in a dormancy model with a pair that is potent in a cholesterol-rich growth environment. These rulesets are predictive, intuitive, and practical, thus enabling rational construction of drug combinations.

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Evaluate the large drug combination space for potential tuberculosis treatments

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In vitro 2-drug combination measurements predict 3–4 drug treatment outcomes in vivo

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Strongly synergistic, antagonistic, or potent drug pairs drive treatment outcome

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Simple rules articulate drug combination design principles for tuberculosis

Computational models, databases and tools for antibiotic combinations

Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.

Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design

Antibiotic resistance is an important public health problem. One potential solution is the development of synergistic antibiotic combinations, in which the combination is more effective than the component drugs. However, experimental progress in this direction is severely limited by the number of samples required to exhaustively test for synergy, which grows exponentially with the number of drugs combined. We introduce a new metric for antibiotic synergy, motivated by the popular Fractional Inhibitory Concentration Index and the Highest Single Agent model. We also propose a new experimental design that samples along all appropriately normalized diagonals in concentration space, and prove that this design identifies all synergies among a set of drugs while only sampling a small fraction of the possible combinations. We applied our method to screen two- through eight-way combinations of eight antibiotics at 10 concentrations each, which requires sampling only 2,560 unique combinations of antibiotic concentrations.

Antibiotic resistance is a growing public health concern, and there is an increasing need for methods to combat it. One potential approach is the development of synergistic antibiotic combinations, in which a mixture of drugs is more effective than any individual component. Unfortunately, the search for clinically beneficial drug combinations is severely restricted by the pace at which drugs can be screened. To date, most studies of combination therapies have been limited to testing only pairs or triples of drugs. These studies have identified primarily antagonistic drug interactions, in which the combination is less effective than the individual components. There is an acute need for methodologies that enable screening of higher-order drug combinations, both to identify synergies among many drugs and to understand the behavior of higher-order combinations. In this work we introduce a new paradigm for combination testing, the normalized diagonal sampling design, that makes identifying interactions among eight or more drugs feasible for the first time. Screening d drugs at m different combinations requires m ⋅ 2^d samples under our design as opposed to m^d under exhaustive screening, while provably identifying all synergies under mild assumptions about antibiotic behavior. Scientists can use our design to quickly screen for antibiotic interactions, accelerating the pace of combination therapy development.

A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions

Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.

Expanding the search for small-molecule antibacterials by multidimensional profiling

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

Prediction of Synergistic Antibiotic Combinations by Graph Learning

Antibiotic resistance is a major public health concern. Antibiotic combinations, offering better efficacy at lower doses, are a useful way to handle this problem. However, it is difficult for us to find effective antibiotic combinations in the vast chemical space. Herein, we propose a graph learning framework to predict synergistic antibiotic combinations. In this model, a network proximity method combined with network propagation was used to quantify the relationships of drug pairs, and we found that synergistic antibiotic combinations tend to have smaller network proximity. Therefore, network proximity can be used for building an affinity matrix. Subsequently, the affinity matrix was fed into a graph regularization model to predict potential synergistic antibiotic combinations. Compared with existing methods, our model shows a better performance in the prediction of synergistic antibiotic combinations and interpretability.

Combinatorial nanodroplet platform for screening antibiotic combinations

The emergence and spread of multidrug resistant bacterial strains and concomitant dwindling of effective antibiotics pose worldwide healthcare challenges. To address these challenges, advanced engineering tools are developed to personalize antibiotic treatments by speeding up the diagnostics that is critical to prevent antibiotic misuse and overuse and make full use of existing antibiotics. Meanwhile, it is necessary to investigate novel antibiotic strategies. Recently, repurposing mono antibiotics into combinatorial antibiotic therapies has shown great potential for treatment of bacterial infections. However, widespread adoption of drug combinations has been hindered by the complexity of screening techniques and the cost of reagent consumptions in practice. In this study, we developed a combinatorial nanodroplet platform for automated and high-throughput screening of antibiotic combinations while consuming orders of magnitude lower reagents than the standard microtiter-based screening method. In particular, the proposed platform is capable of creating nanoliter droplets with multiple reagents in an automatic manner, tuning concentrations of each component, performing biochemical assays with high flexibility (e.g., temperature and duration), and achieving detection with high sensitivity. A biochemical assay, based on the reduction of resazurin by the metabolism of bacteria, has been characterized and employed to evaluate the combinatorial effects of the antibiotics of interest. In a pilot study, we successfully screened pairwise combinations between 4 antibiotics for a model Escherichia coli strain.

Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis

Lengthy multidrug chemotherapy is required to achieve a durable cure in tuberculosis. However, we lack well-validated, high-throughput in vitro models that predict animal outcomes. Here, we provide an extensible approach to rationally prioritize combination therapies for testing in in vivo mouse models of tuberculosis. We systematically measured Mycobacterium tuberculosis response to all two- and three-drug combinations among ten antibiotics in eight conditions that reproduce lesion microenvironments, resulting in >500,000 measurements. Using these in vitro data, we developed classifiers predictive of multidrug treatment outcome in a mouse model of disease relapse and identified ensembles of in vitro models that best describe in vivo treatment outcomes. We identified signatures of potencies and drug interactions in specific in vitro models that distinguish whether drug combinations are better than the standard of care in two important preclinical mouse models. Our framework is generalizable to other difficult-to-treat diseases requiring combination therapies. A record of this paper's transparent peer review process is included in the supplemental information.

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available.

IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to additional organisms and conditions and could be used to improve clinical AST by identifying the key regulatory factors dictating antibiotic susceptibility.

Deciphering the mechanism of action of antitubercular compounds with metabolomics

Tuberculosis (TB), one of the oldest and deadliest bacterial diseases, continues to cause serious global economic, health, and social problems. Current TB treatments are lengthy, expensive, and routinely ineffective against emerging drug resistant strains. Thus, there is an urgent need for the identification and development of novel TB drugs possessing comprehensive and specific mechanisms of action (MoAs). Metabolomics is a valuable approach to elucidating the MoA, toxicity, and potency of promising chemical leads, which is a critical step of the drug discovery process. Recent advances in metabolomics methodologies for deciphering MoAs include high-throughput screening techniques, the integration of multiple omics methods, mass spectrometry imaging, and software for automated analysis. This review describes recently introduced metabolomics methodologies and techniques for drug discovery, highlighting specific applications to the discovery of new antitubercular drugs and the elucidation of their MoAs.

The future of antibiotics begins with discovering new combinations

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

Mechanistic insights into synergy between nalidixic acid and tetracycline against clinical isolates of Acinetobacter baumannii and Escherichia coli

The increasing prevalence of antimicrobial resistance has become a global health problem. Acinetobacter baumannii is an important nosocomial pathogen due to its capacity to persist in the hospital environment. It has a high mortality rate and few treatment options. Antibiotic combinations can help to fight multi-drug resistant (MDR) bacterial infections, but they are rarely used in the clinics and mostly unexplored. The interaction between bacteriostatic and bactericidal antibiotics are mostly reported as antagonism based on the results obtained in the susceptible model laboratory strain Escherichia coli. However, in the present study, we report a synergistic interaction between nalidixic acid and tetracycline against clinical multi-drug resistant A. baumannii and E. coli. Here we provide mechanistic insight into this dichotomy. The synergistic combination was studied by checkerboard assay and time-kill curve analysis. We also elucidate the mechanism behind this synergy using several techniques such as fluorescence spectroscopy, flow cytometry, fluorescence microscopy, morphometric analysis, and real-time polymerase chain reaction. Nalidixic acid and tetracycline combination displayed synergy against most of the MDR clinical isolates of A. baumannii and E. coli but not against susceptible isolates. Finally, we demonstrate that this combination is also effective in vivo in an A. baumannii/Caenorhabditis elegans infection model (p < 0.001).

Systems-Level Chemical Biology to Accelerate Antibiotic Drug Discovery

Drug-resistant bacterial infections pose an imminent and growing threat to public health. The discovery and development of new antibiotics of novel chemical class and mode of action that are unsusceptible to existing resistance mechanisms is imperative for tackling this threat. Modern industrial drug discovery, however, has failed to provide new drugs of this description, as it is dependent largely on a reductionist genes-to-drugs research paradigm. We posit that the lack of success in new antibiotic drug discovery is due in part to a lack of understanding of the bacterial cell system as whole. A fundamental understanding of the architecture and function of bacterial systems has been elusive but is of critical importance to design strategies to tackle drug-resistant bacterial pathogens.Increasingly, systems-level approaches are rewriting our understanding of the cell, defining a dense network of redundant and interacting components that resist perturbations of all kinds, including by antibiotics. Understanding the network properties of bacterial cells requires integrative, systematic, and genome-scale approaches. These methods strive to understand how the phenotypic behavior of bacteria emerges from the many interactions of individual molecular components that constitute the system. With the ability to examine genomic, transcriptomic, proteomic, and metabolomic consequences of, for example, genetic or chemical perturbations, researchers are increasingly moving away from one-gene-at-a-time studies to consider the system-wide response of the cell. Such measurements are demonstrating promise as quantitative tools, powerful discovery engines, and robust hypothesis generators with great value to antibiotic drug discovery.In this Account, we describe our thinking and findings using systems-level studies aimed at understanding bacterial physiology broadly and in uncovering new antibacterial chemical matter of novel mechanism. We share our systems-level toolkit and detail recent technological developments that have enabled unprecedented acquisition of genome-wide interaction data. We focus on three types of interactions: gene-gene, chemical-gene, and chemical-chemical. We provide examples of their use in understanding cell networks and how these insights might be harnessed for new antibiotic discovery. By example, we show the application of these principles in mapping genetic networks that underpin phenotypes of interest, characterizing genes of unknown function, validating small-molecule screening platforms, uncovering novel chemical probes and antibacterial leads, and delineating the mode of action of antibacterial chemicals. We also discuss the importance of computation to these approaches and its probable dominance as a tool for systems approaches in the future. In all, we advocate for the use of systems-based approaches as discovery engines in antibacterial research, both as powerful tools and to stimulate innovation.

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

Perspectives on the antibiotic contamination, resistance, metabolomics, and systemic remediation

Antibiotics have been regarded as the emerging contaminants because of their massive use in humans and veterinary medicines and their persistence in the environment. The global concern of antibiotic contamination to different environmental matrices and the emergence of antibiotic resistance has posed a severe impact on the environment. Different mass-spectrometry-based techniques confirm their presence in the environment. Antibiotics are released into the environment through the wastewater steams and runoff from land application of manure. The microorganisms get exposed to the antibiotics resulting in the development of antimicrobial resistance. Consistent release of the antibiotics, even in trace amount into the soil and water ecosystem, is the major concern because the antibiotics can lead to multi-resistance in bacteria which can cause hazardous effects on agriculture, aquaculture, human, and livestock. A better understanding of the correlation between the antibiotic use and occurrence of antibiotic resistance can help in the development of policies to promote the judicious use of antibiotics. The present review puts a light on the remediation, transportation, uptake, and antibiotic resistance in the environment along with a novel approach of creating a database for systemic remediation, and metabolomics for the cleaner and safer environment.

Guided screen for synergistic three-drug combinations

Combinations of three or more drugs are routinely used in various medical fields such as clinical oncology and infectious diseases to prevent resistance or to achieve synergistic therapeutic benefits. The very large number of possible high-order drug combinations presents a formidable challenge for discovering synergistic drug combinations. Here, we establish a guided screen to discover synergistic three-drug combinations. Using traditional checkerboard and recently developed diagonal methods, we experimentally measured all pairwise interactions among eight compounds in Erwinia amylovora, the causative agent of fire blight. Showing that synergy measurements of these two methods agree, we predicted synergy/antagonism scores for all possible three-drug combinations by averaging the synergy scores of pairwise interactions. We validated these predictions by experimentally measuring 35 three-drug interactions. Therefore, our guided screen for discovering three-drug synergies is (i) experimental screen of all pairwise interactions using diagonal method, (ii) averaging pairwise scores among components to predict three-drug interaction scores, (iii) experimental testing of top predictions. In our study, this strategy resulted in a five-fold reduction in screen size to find the most synergistic three-drug combinations.

Biology of antimicrobial resistance and approaches to combat it

Insufficient development of new antibiotics and the rising resistance of bacteria to those that we have are putting the world at risk of losing the most widely curative class of medicines currently available. Preventing deaths from antimicrobial resistance (AMR) will require exploiting emerging knowledge not only about genetic AMR conferred by horizontal gene transfer or de novo mutations but also about phenotypic AMR, which lacks a stably heritable basis. This Review summarizes recent advances and continuing limitations in our understanding of AMR and suggests approaches for combating its clinical consequences, including identification of previously unexploited bacterial targets, new antimicrobial compounds, and improved combination drug regimens.

Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas

Tuberculosis (TB) remains as one of the world's deadliest infectious diseases despite the use of standardized antibiotic therapies. Recommended therapy for drug-susceptible TB is up to 6 months of antibiotics. Factors that contribute to lengthy regimens include antibiotic underexposure in lesions due to poor pharmacokinetics (PK) and complex granuloma compositions, but it is difficult to quantify how individual antibiotics are affected by these factors and to what extent these impact treatments. We use our next-generation multi-scale computational model to simulate granuloma formation and function together with antibiotic pharmacokinetics and pharmacodynamics, allowing us to predict conditions leading to granuloma sterilization. In this work, we focus on how PK variability, determined from human PK data, and granuloma heterogeneity each quantitatively impact granuloma sterilization. We focus on treatment with the standard regimen for TB of four first-line antibiotics: isoniazid, rifampin, ethambutol, and pyrazinamide. We find that low levels of antibiotic concentration due to naturally occurring PK variability and complex granulomas leads to longer granuloma sterilization times. Additionally, the ability of antibiotics to distribute in granulomas and kill different subpopulations of bacteria contributes to their specialization in the more efficacious combination therapy. These results can inform strategies to improve antibiotic therapy for TB.

Repurposed drug candidates for antituberculosis therapy

Antibiotics have been a key part of clinical treatments for more than 70 years. Long-term use of antimicrobial treatments has led to the development of severe bacterial resistance, which has become increasingly serious due to antibiotic abuse, resulting in the treatment of bacterial infections becoming challenging. The repurposing of approved drugs presents a promising strategy to address current bottlenecks in the development of novel antibacterial agents. Drug repurposing is a cost-effective emerging strategy, which aims to treat resistant infectious diseases by identifying known drugs with predicted efficacy for diseases other than the target disease. This strategy has potential in the treatment of tuberculosis (TB), particularly drug-resistant TB. In recent years, a panel of drugs approved for clinical use or clinical trials, such as linezolid, vancomycin and celecoxib, have been found to have anti-TB activities. However, the utility of drug repurposing is limited by the number of candidate compounds and their low activities. The low activities of repurposed drugs have slowed the development of a drug-repurposing strategy for anti-TB drugs. The present review discusses progress in the discovery of new anti-TB agents through drug repurposing since 2014. We also discuss the challenges faced and analyze the innovative ways that are being used to overcome these difficulties. This review may provide a useful guide for researchers in the field of drug repurposing.

Prediction of drug combination effects with a minimal set of experiments

High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here we implemented DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose-response measurements for accurate prediction of drug combination synergy and antagonism. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines, and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully-measured dose-response matrices. Measuring only the diagonal of the matrix provides an accurate and practical option for combinatorial screening. The open-source web-implementation enables applications of DECREASE to both pre-clinical and translational studies.

Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

Multidrug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10⁻⁴) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.

Design of high-order antibiotic combinations against M. tuberculosis by ranking and exclusion

Combinations of more than two drugs are routinely used for the treatment of pathogens and tumors. High-order combinations may be chosen due to their non-overlapping resistance mechanisms or for favorable drug interactions. Synergistic/antagonistic interactions occur when the combination has a higher/lower effect than the sum of individual drug effects. The standard treatment of Mycobacterium tuberculosis (Mtb) is an additive cocktail of three drugs which have different targets. Herein, we experimentally measured all 190 pairwise interactions among 20 antibiotics against Mtb growth. We used the pairwise interaction data to rank all possible high-order combinations by strength of synergy/antagonism. We used drug interaction profile correlation as a proxy for drug similarity to establish exclusion criteria for ideal combination therapies. Using this ranking and exclusion design (R/ED) framework, we modeled ways to improve the standard 3-drug combination with the addition of new drugs. We applied this framework to find the best 4-drug combinations against drug-resistant Mtb by adding new exclusion criteria to R/ED. Finally, we modeled alternating 2-order combinations as a cycling treatment and found optimized regimens significantly reduced the overall effective dose. R/ED provides an adaptable framework for the design of high-order drug combinations against any pathogen or tumor.

Antimicrobial Drug Interactions: Systematic Evaluation of Protein and Nucleic Acid Synthesis Inhibitors

Antimicrobial multidrug resistance and its transmission among strains are serious problems. Success rate is decreased and treatment options are narrowed due to increasing bacterial multidrug resistance. On the other hand, the need for long-term efforts to discover new antibiotics and difficulties finding new treatment protocols make this problem more complex. Combination therapy, especially with synergistic use of antimicrobials is a rational treatment option with huge benefits. Thus, screening antibiotic interactions is crucial for finding better treatment options. Clinicians currently use combinatorial antibiotic treatment as an effective treatment option. However, antibiotics can show synergistic or antagonistic interactions when used together. In our study, we aimed to investigate interactions of antibiotics with different mechanisms of action. Antibiotics, which act as protein synthesis inhibitors (P) and nucleic acid synthesis inhibitors (N) were used in our study. We tested 66 (PN), 15 (NN), and 55 (PP) drug pairs on the Escherichia coli strain. The Loewe additivity model was used and alpha scores were calculated for analysis of interactions of drug combinations. Drug interactions were categorized as synergistic or antagonistic. Accordingly, pairwise combinations of protein synthesis inhibitors (PP) showed stronger synergistic interactions than those of nucleic acid synthesis inhibitors (NN) and nucleic acid synthesis–protein synthesis inhibitors (PN). As a result, the importance of mechanisms of action of drugs is emphasized in the selection of synergistic drug combinations.

Miniaturized Checkerboard Assays to Measure Antibiotic Interactions

Drugs may have synergistic or antagonistic interactions when combined. Checkerboard assays, where two drugs are combined in many doses, allow sensitive measurement of drug interactions. Here, we describe a protocol to measure the pairwise interactions among three antibiotics, in duplicate, in 5 days, using only two 96-well microplates and standard laboratory equipment.

Metabolomics-Driven Exploration of the Chemical Drug Space to Predict Combination Antimicrobial Therapies

Alternative to the conventional search for single-target, single-compound treatments, combination therapies can open entirely new opportunities to fight antibiotic resistance. However, combinatorial complexity prohibits experimental testing of drug combinations on a large scale, and methods to rationally design combination therapies are lagging behind. Here, we developed a combined experimental-computational approach to predict drug-drug interactions using high-throughput metabolomics. The approach was tested on 1,279 pharmacologically diverse drugs applied to the gram-negative bacterium Escherichia coli. Combining our metabolic profiling of drug response with previously generated metabolic and chemogenomic profiles of 3,807 single-gene deletion strains revealed an unexpectedly large space of inhibited gene functions and enabled rational design of drug combinations. This approach is applicable to other therapeutic areas and can unveil unprecedented insights into drug tolerance, side effects, and repurposing. The compendium of drug-associated metabolome profiles is available at https://zampierigroup.shinyapps.io/EcoPrestMet, providing a valuable resource for the microbiological and pharmacological communities.

Drug combinations: a strategy to extend the life of antibiotics in the 21st century

Antimicrobial resistance threatens a resurgence of life-threatening bacterial infections and the potential demise of many aspects of modern medicine. Despite intensive drug discovery efforts, no new classes of antibiotics have been developed into new medicines for decades, in large part owing to the stringent chemical, biological and pharmacological requisites for effective antibiotic drugs. Combinations of antibiotics and of antibiotics with non-antibiotic activity-enhancing compounds offer a productive strategy to address the widespread emergence of antibiotic-resistant strains. In this Review, we outline a theoretical and practical framework for the development of effective antibiotic combinations.

Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment

Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework—Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.

The antibiotic resistance epidemic has created a pressing need to understand factors that influence antibiotic efficacy. An often-overlooked factor in the search for new treatments is the pathogen environment. Understanding the differences in pathogen sensitivity to antibiotics in lab conditions versus inside the host is necessary for translating new discoveries into the clinic. Hence, we experimentally measured the sensitivity of E. coli to drugs and drug combinations in different metabolic conditions. Our data revealed that the environment dramatically changes treatment potency. Each antibiotic class was affected uniquely by each metabolic condition. The large number of metabolic conditions inside the host greatly complicates the identification of effective therapies. To address this challenge, we present a computational approach called MAGENTA that accurately predicted efficacy of antibiotic regimens in different conditions, which we confirmed experimentally. Furthermore, we show that MAGENTA can be applied to other bacterial pathogens such as A. baumannii and M. tuberculosis without the need for generating expensive data in each organism. MAGENTA accurately predicted efficacy in the pathogen A. baumannii using data from E. coli by identifying genes that are common between the two bacteria. Our study revealed the significant yet predictable impact of environment on drug combination potency.

Predicting Drug Interactions From Chemogenomics Using INDIGO

Designing effective antibiotic combination regimens is critical for countering drug resistance in pathogens. Yet the large combinatorial search-space makes the identification of effective combinations a significant challenge. There is a great need for computational approaches that can rapidly prioritize potential combination regimens based on the antagonistic and synergistic interactions among the constituent antibiotics. This protocol outlines the steps to predict antibiotic interactions from chemogenomics data using the INDIGO algorithm. INDIGO predicted novel drug-drug interaction outcomes quantitatively with high accuracy based on experimental evaluation of predictions in E. coli and S. aureus, and it overcomes several limitations of existing drug-interaction prediction algorithms. The INDIGO approach also expands the applicability of chemogenomic data from model organisms to a broader set of less-studied pathogens. INDIGO can predict drug-interaction outcomes in the bacterial pathogens S. aureus and M. tuberculosis, using chemogenomics data from E. coli by quantifying the degree of conservation of the drug-gene interaction network between different species. The INDIGO approach, which is demonstrated for E. coli and S. aureus in this protocol, can be applied easily to other organisms including pathogens.

Use of genetic and chemical synthetic lethality as probes of complexity in bacterial cell systems

Different conditions and genomic contexts are known to have an impact on gene essentiality and interactions. Synthetic lethal interactions occur when a combination of perturbations, either genetic or chemical, result in a more profound fitness defect than expected based on the effect of each perturbation alone. Synthetic lethality in bacterial systems has long been studied; however, during the past decade, the emerging fields of genomics and chemical genomics have led to an increase in the scale and throughput of these studies. Here, we review the concepts of genomics and chemical genomics in the context of synthetic lethality and their revolutionary roles in uncovering novel biology such as the characterization of genes of unknown function and in antibacterial drug discovery. We provide an overview of the methodologies, examples and challenges of both genetic and chemical synthetic lethal screening platforms. Finally, we discuss how to apply genetic and chemical synthetic lethal approaches to rationalize the synergies of drugs, screen for new and improved antibacterial therapies and predict drug mechanism of action.

Additivity of inhibitory effects in multidrug combinations

From natural ecology^1-4 to clinical therapy^5-8, cells are often exposed to mixtures of multiple drugs. Two competing null models are used to predict the combined effect of drugs: response additivity (Bliss) and dosage additivity (Loewe)^9-11. Here, noting that these models diverge with increased number of drugs, we contrast their predictions with growth measurements of four phylogenetically distant microorganisms including Escherichia coli, Staphylococcus aureus, Enterococcus faecalis and Saccharomyces cerevisiae, under combinations of up to ten different drugs. In all species, as the number of drugs increases, Bliss maintains accuracy while Loewe systematically loses its predictive power. The total dosage required for growth inhibition, which Loewe predicts should be fixed, steadily increases with the number of drugs, following a square-root scaling. This scaling is explained by an approximation to Bliss where, inspired by R. A. Fisher's classical geometric model¹², dosages of independent drugs add up as orthogonal vectors rather than linearly. This dose-orthogonality approximation provides results similar to Bliss, yet uses the dosage language as in Loewe and is hence easier to implement and intuit. The rejection of dosage additivity in favour of effect additivity and dosage orthogonality provides a framework for understanding how multiple drugs and stressors add up in nature and the clinic.