Drug antagonism and single-agent dominance result from differences in death kinetics

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.

Functional genomic screens with death rate analyses reveal mechanisms of drug action

A common approach for understanding how drugs induce their therapeutic effects is to identify the genetic determinants of drug sensitivity. Because 'chemo-genetic profiles' are performed in a pooled format, inference of gene function is subject to several confounding influences related to variation in growth rates between clones. In this study, we developed Method for Evaluating Death Using a Simulation-assisted Approach (MEDUSA), which uses time-resolved measurements, along with model-driven constraints, to reveal the combination of growth and death rates that generated the observed drug response. MEDUSA is uniquely effective at identifying death regulatory genes. We apply MEDUSA to characterize DNA damage-induced lethality in the presence and absence of p53. Loss of p53 switches the mechanism of DNA damage-induced death from apoptosis to a non-apoptotic death that requires high respiration. These findings demonstrate the utility of MEDUSA both for determining the genetic dependencies of lethality and for revealing opportunities to potentiate chemo-efficacy in a cancer-specific manner.

Functional genomics reveals an off-target dependency of drug synergy in gastric cancer therapy

The rational combination of anticancer agents is critical to improving patient outcomes in cancer. Nonetheless, most combination regimens in the clinic result from empirical methodologies disregarding insight into the mechanism of action and missing the opportunity to improve therapy outcomes incrementally. Deciphering the genetic dependencies and vulnerabilities responsible for synergistic interactions is crucial for rationally developing effective anticancer drug combinations. Hence, we screened pairwise pharmacological interactions between molecular-targeted agents and conventional chemotherapeutics and examined the genome-scale genetic dependencies in gastric adenocarcinoma cell models. Since this type of cancer is mainly chemoresistant and incurable, clinical situations demand effective combination strategies. Our pairwise combination screen revealed SN38/erlotinib as the drug pair with the most robust synergism. Genome-wide CRISPR screening and a shRNA-based signature assay indicated that the genetic dependency/vulnerability signature of SN38/erlotinib is the same as SN38 alone. Additional investigation revealed that the enhanced cell death with improved death kinetics caused by the SN38/erlotinib combination is surprisingly due to erlotinib's off-target effect that inhibits ABCG2 but not its on-target effect on EGFR. Our results confirm that a genetic dependency signature different from the single-drug application may not be necessary for the synergistic interaction of molecular-targeted agents with conventional chemotherapeutics in gastric adenocarcinoma. The findings also demonstrated the efficacy of functional genomics approaches in unveiling biologically validated mechanisms of pharmacological interactions.

Mapping resistance-associated anthelmintic interactions in the model nematode Caenorhabditis elegans

Parasitic nematodes infect billions of people and are mainly controlled by anthelmintic mass drug administration (MDA). While there are growing efforts to better understand mechanisms of anthelmintic resistance in human and animal populations, it is unclear how resistance mechanisms that alter susceptibility to one drug affect the interactions and efficacy of drugs used in combination. Mutations that alter drug permeability across primary nematode barriers have been identified as potential resistance mechanisms using the model nematode Caenorhabditis elegans. We leveraged high-throughput assays in this model system to measure altered anthelmintic susceptibility in response to genetic perturbations of potential cuticular, amphidial, and alimentary routes of drug entry. Mutations in genes associated with these tissue barriers differentially altered susceptibility to the major anthelmintic classes (macrocyclic lactones, benzimidazoles, and nicotinic acetylcholine receptor agonists) as measured by animal development. We investigated two-way anthelmintic interactions across C. elegans genetic backgrounds that confer resistance or hypersensitivity to one or more drugs. We observe that genetic perturbations that alter susceptibility to a single drug can shift the drug interaction landscape and lead to the appearance of novel synergistic and antagonistic interactions. This work establishes a framework for investigating combinatorial therapies in model nematodes that can potentially be translated to amenable parasite species.

Effect of storage age and containers on the physicochemical degradation of guggul oleo-resin

Guggul is a gum oleo-resin, tapped from a data deficient plant- Commiphora wightii (Arnott.) Bhandari in India. It is extensively used in ayurvedic drugs and formulations since ages. Natural plant-based products; especially aromatic ones like guggul gum oleo-resin deteriorates, qualitatively during its storage and transits before reaching the industry for its value addition. This economical and ecological loss can be avoided if it is stored in proper containers. Physico-chemical degradation of guggul samples stored were analysed by scanned electron microscopy, fourier transformed infra red, thermogravimatric, Powdered X-ray diffraction and elemental analysis for carbon, hydrogen, nitrogen and sulphur. Physico-chemical degradation of guggul oleo-resin occurs with the age of storage and the type of storage containers used. Among the four storage containers (earthen pot, plastic jar, polythene bag, jute bag) evaluated, earthen pot was found to be the best in checking the qualitative loss of guggul even upto 24 months. The qualitative information generated in the study on guggul storage may be useful to the drug industry and guggul traders. It may encourage them practice storing guggul in earthen pots against current practice of using jute bags and polythene bags, to store it.

Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects

Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies.

p53 controls choice between apoptotic and non-apoptotic death following DNA damage

DNA damage can activate apoptotic and non-apoptotic forms of cell death; however, it remains unclear what features dictate which type of cell death is activated. We report that p53 controls the choice between apoptotic and non-apoptotic death following exposure to DNA damage. In contrast to the conventional model, which suggests that p53-deficient cells should be resistant to DNA damage-induced cell death, we find that p53-deficient cells die at high rates following DNA damage, but exclusively using non-apoptotic mechanisms. Our experimental data and computational modeling reveal that non-apoptotic death in p53-deficient cells has not been observed due to use of assays that are either insensitive to cell death, or that specifically score apoptotic cells. Using functional genetic screening - with an analysis that enables computational inference of the drug-induced death rate - we find in p53-deficient cells that DNA damage activates a mitochondrial respiration-dependent form of cell death, called MPT-driven necrosis. Cells deficient for p53 have high basal respiration, which primes MPT-driven necrosis. Finally, using metabolite profiling, we identified mitochondrial activity-dependent metabolic vulnerabilities that can be targeted to potentiate the lethality of DNA damage specifically in p53-deficient cells. Our findings reveal how the dual functions of p53 in regulating mitochondrial activity and the DNA damage response combine to facilitate the choice between apoptotic and non-apoptotic death.

Mapping resistance-associated anthelmintic interactions in the model nematode Caenorhabditis elegans

Parasitic nematodes infect billions of people and are mainly controlled by anthelmintic mass drug administration (MDA). While there are growing efforts to better understand mechanisms of anthelmintic resistance in human and animal populations, it is unclear how resistance mechanisms that alter susceptibility to one drug affect the interactions and efficacy of drugs used in combination. Mutations that alter drug permeability across primary nematode barriers have been identified as potential resistance mechanisms using the model nematode Caenorhabditis elegans. We leveraged high-throughput assays in this model system to measure altered anthelmintic susceptibility in response to genetic perturbations of potential cuticular, amphidial, and alimentary routes of drug entry. Mutations in genes associated with these tissue barriers differentially altered susceptibility to the major anthelmintic classes (macrocyclic lactones, benzimidazoles, and nicotinic acetylcholine receptor agonists) as measured by animal development. We investigated two-way anthelmintic interactions across C. elegans genetic backgrounds that confer resistance or hypersensitivity to one or more drugs. We observe that genetic perturbations that alter susceptibility to a single drug can shift the drug interaction landscape and lead to the appearance of novel synergistic and antagonistic interactions. This work establishes a framework for investigating combinatorial therapies in model nematodes that can potentially be translated to amenable parasite species.

Surveying the landscape of emerging and understudied cell death mechanisms

Cell death can be a highly regulated process. A large and growing number of mammalian cell death mechanisms have been described over the past few decades. Major pathways with established roles in normal or disease biology include apoptosis, necroptosis, pyroptosis and ferroptosis. However, additional non-apoptotic cell death mechanisms with unique morphological, genetic, and biochemical features have also been described. These mechanisms may play highly specialized physiological roles or only become activated in response to specific lethal stimuli or conditions. Understanding the nature of these emerging and understudied mechanisms may provide new insight into cell death biology and suggest new treatments for diseases such as cancer and neurodegeneration.

Subcellular Quantitation of ADP-Ribosylation by High-Content Microscopy

ADP-ribosylation is a posttranslational modification with many functions ranging from the DNA damage response to transcriptional regulation. While nuclear ADP-ribosylation has been extensively studied in the context of genotoxic stress mediated by PARP1, signaling by other members of the family and in other cellular compartments is still not as well understood. In recent years, however, progress has been made with the development of new tools for detection of ADP-ribosylation by immunofluorescence, which allows for a spatial differentiation of signal intensity for different cellular compartments. Here, we present our method for the detection and quantification of compartment-specific ADP-ribosylation by immunofluorescence and show why the engineered macrodomain eAf5121 might be the best tool to date.

Materials-driven approaches to understand extrinsic drug resistance in cancer

Metastatic cancer has a poor prognosis, because it is broadly disseminated and associated with both intrinsic and acquired drug resistance. Critical unmet needs in effectively killing drug resistant cancer cells include overcoming the drug desensitization characteristics of some metastatic cancers/lesions, and tailoring therapeutic regimens to both the tumor microenvironment and the genetic profiles of the resident cancer cells. Bioengineers and materials scientists are developing technologies to determine how metastatic sites exclude therapies, and how extracellular factors (including cells, proteins, metabolites, extracellular matrix, and abiotic factors) at metastatic sites significantly affect drug pharmacodynamics. Two looming challenges are determining which feature, or combination of features, from the tumor microenvironment drive drug resistance, and what the relative impact is of extracellular signals vs. intrinsic cell genetics in determining drug response. Sophisticated systems biology tools that can de-convolve a crowded network of signals and responses, as well as controllable microenvironments capable of providing discrete and tunable extracellular cues can help us begin to interrogate the high dimensional interactions governing drug resistance in patients.

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.

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.

Machine learning methods, databases and tools for drug combination prediction

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

Antagonistic Interaction between Histone Deacetylase Inhibitor: Cambinol and Cisplatin-An Isobolographic Analysis in Breast Cancer In Vitro Models

Breast cancer (BC) is the leading cause of death in women all over the world. Currently, combined chemotherapy with two or more agents is considered a promising anti-cancer tool to achieve better therapeutic response and to reduce therapy-related side effects. In our study, we demonstrated an antagonistic effect of cytostatic agent-cisplatin (CDDP) and histone deacetylase inhibitor: cambinol (CAM) for breast cancer cell lines with different phenotypes: estrogen receptor positive (MCF7, T47D) and triple negative (MDA-MB-231, MDA-MB-468). The type of pharmacological interaction was assessed by an isobolographic analysis. Our results showed that both agents used separately induced cell apoptosis; however, applying them in combination ameliorated antiproliferative effect for all BC cell lines indicating antagonistic interaction. Cell cycle analysis showed that CAM abolished cell cycle arrest in S phase, which was induced by CDDP. Additionally, CAM increased cell proliferation compared to CDDP used alone. Our data indicate that CAM and CDDP used in combination produce antagonistic interaction, which could inhibit anti-cancer treatment efficacy, showing importance of preclinical testing.

A compendium of kinetic modulatory profiles identifies ferroptosis regulators

Cell death can be executed by regulated apoptotic and nonapoptotic pathways, including the iron-dependent process of ferroptosis. Small molecules are essential tools for studying the regulation of cell death. Using time-lapse imaging and a library of 1,833 bioactive compounds, we assembled a large compendium of kinetic cell death modulatory profiles for inducers of apoptosis and ferroptosis. From this dataset we identify dozens of ferroptosis suppressors, including numerous compounds that appear to act via cryptic off-target antioxidant or iron chelating activities. We show that the FDA-approved drug bazedoxifene acts as a potent radical trapping antioxidant inhibitor of ferroptosis both in vitro and in vivo. ATP-competitive mechanistic target of rapamycin (mTOR) inhibitors, by contrast, are on-target ferroptosis inhibitors. Further investigation revealed both mTOR-dependent and mTOR-independent mechanisms that link amino acid metabolism to ferroptosis sensitivity. These results highlight kinetic modulatory profiling as a useful tool to investigate cell death regulation.

Drug GRADE: An Integrated Analysis of Population Growth and Cell Death Reveals Drug-Specific and Cancer Subtype-Specific Response Profiles

When evaluating anti-cancer drugs, two different measurements are used: relative viability, which scores an amalgam of proliferative arrest and cell death, and fractional viability, which specifically scores the degree of cell killing. We quantify relationships between drug-induced growth inhibition and cell death by counting live and dead cells using quantitative microscopy. We find that most drugs affect both proliferation and death, but in different proportions and with different relative timing. This causes a non-uniform relationship between relative and fractional response measurements. To unify these measurements, we created a data visualization and analysis platform called drug GRADE, which characterizes the degree to which death contributes to an observed drug response. GRADE captures drug- and genotype-specific responses, which are not captured using traditional pharmacometrics. This study highlights the idiosyncratic nature of drug-induced proliferative arrest and cell death. Furthermore, we provide a metric for quantitatively evaluating the relationship between these behaviors.

FLICK: an optimized plate reader-based assay to infer cell death kinetics

Evaluating drug sensitivity is improved by directly quantifying death kinetics, rather than correlates of viability, such as metabolic activity. This is challenging, requiring time-lapse microscopy and genetically encoded labels to distinguish live and dead cells. Here, we describe fluorescence-based and lysis-dependent inference of cell death kinetics (FLICK). This method requires only a standard fluorescence plate reader, retaining the high-throughput nature and broad accessibility of common viability assays. However, FLICK specifically quantifies death, including an accurate inference of death kinetics.

For complete details on the use and execution of this protocol, please refer to Richards et al. (2020).

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FLICK generates a death-specific measure of drug response not a correlate of viability

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FLICK is high-throughput and easy to use like common plate reader-based assays

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FLICK can be used to generate any commonly used drug response metric

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FLICK can be used to infer cell death kinetics

Evaluation of the cytotoxic and immunogenic potential of temozolamide, panobinostat, and Lophophora williamsii extract against C6 glioma cells

Glioblastoma multiforme is a malignant neoplasm of the brain with poor prognosis. The first-line drug against glioblastoma is the alkylating agent temozolamide (TMZ); unfortunately, treatment resistance and tumor re-incidence are common. In some cases, immunogenic cell death (ICD) inducers can decrease treatment resistance and tumor recurrence by stimulating an antitumor specific immune response. Not all ICD inducers, however, are suitable for glioma patients because of the low permeability of the blood-brain barrier (BBB). Panobinostat (PAN), a histone deacetylase inhibitor and Lophophora williamsii (LW) extract can pass through the BBB and have antitumor properties. The aim of this study is to evaluate the cytotoxic potential of TMZ, PAN and LW extract against the glioma C6 cell line, and its role in the release of damage-associated molecular patterns (DAMPs), which is a hallmark of ICD. Our results indicate that all treatments induce cellular death in a time- and concentration-dependent manner, and that PAN and LW extract induce apoptosis, whereas TMZ induces apoptosis and necrosis. Also, that some of the treatments and their sequential administration induce the release of DAMPs. Furthermore, in a rat glioma model, we observed that all treatments decreased tumor volume, but the in vivo cell death mechanism was not ICD. Our findings indicate that TMZ, PAN, and LW combination have a cytotoxic effect against glioma cells but do not induce ICD.

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2

Antiviral drug development for coronavirus disease 2019 (COVID-19) is occurring at an unprecedented pace, yet there are still limited therapeutic options for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2, thus generating better antiviral efficacy. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Sixteen synergistic and eight antagonistic combinations were identified; among 16 synergistic cases, combinations of the US Food and Drug Administration (FDA)-approved drug nitazoxanide with remdesivir, amodiaquine, or umifenovir were most notable, all exhibiting significant synergy against SARS-CoV-2 in a cell model. However, the combination of remdesivir and lysosomotropic drugs, such as hydroxychloroquine, demonstrated strong antagonism. Overall, these results highlight the utility of drug repurposing and preclinical testing of drug combinations for discovering potential therapies to treat COVID-19.

ELP-dependent expression of MCL1 promotes resistance to EGFR inhibition in triple-negative breast cancer cells

Targeted therapeutics for cancer generally exploit "oncogene addiction," a phenomenon in which the growth and survival of tumor cells depend on the activity of a particular protein. However, the efficacy of oncogene-targeted therapies varies substantially. For instance, targeting epidermal growth factor receptor (EGFR) signaling is effective in some non-small cell lung cancer (NSCLC) but not in triple-negative breast cancer (TNBC), although these cancers show a similar degree of increase in EGFR activity. Using a genome-wide CRISPR-Cas9 genetic knockout screen, we found that the Elongator (ELP) complex mediates insensitivity to the EGFR inhibitor erlotinib in TNBC cells by promoting the synthesis of the antiapoptotic protein Mcl-1. Depleting ELP proteins promoted apoptotic cell death in an EGFR inhibition-dependent manner. Pharmacological inhibition of Mcl-1 synergized with EGFR inhibition in a panel of genetically diverse TNBC cells. The findings indicate that TNBC "addiction" to EGFR signaling is masked by the ELP complex and that resistance to EGFR inhibitors in TNBC might be overcome by cotargeting Mcl-1.

Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro

COVID-19 is undoubtedly the most impactful viral disease of the current century, afflicting millions worldwide. As yet, there is not an approved vaccine, as well as limited options from existing drugs for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro . Overall, we identified 16 synergistic and 8 antagonistic combinations, 4 of which were both synergistic and antagonistic in a dose-dependent manner. Among the 16 synergistic cases, combinations of nitazoxanide with three other compounds (remdesivir, amodiaquine and umifenovir) were the most notable, all exhibiting significant synergy against SARS-CoV-2. The combination of nitazoxanide, an FDA-approved drug, and remdesivir, FDA emergency use authorization for the treatment of COVID-19, demonstrate a strong synergistic interaction. Notably, the combination of remdesivir and hydroxychloroquine demonstrated strong antagonism. Overall, our results emphasize the importance of both drug repurposing and preclinical testing of drug combinations for potential therapeutic use against SARS-CoV-2 infections.