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Novel psychoactive substances (NPS) refer to synthetic compounds or derivatives of more widely known substances of abuse that have emerged over the last two decades. Case reports suggest that users combine substances to achieve desired psychotropic experiences while reducing dysphoria and unpleasant somatic effects. However, the pattern of combining NPS has not been studied on a large scale. Here, we show that posts discussing NPS describe combining nootropics with sedative-hypnotics and stimulants with plant hallucinogens or psychiatric medications. Discussions that mention sedative-hypnotics most commonly also mention hallucinogens and stimulants. We analyzed 20 years of publicly available posts from Lycaeum, an Internet forum dedicated to sharing information about psychoactive substance use. We used techniques from natural language processing and machine learning to identify NPS and correlate patterns of co-mentions of substances across posts. We found that conversations mentioning synthetic hallucinogens tended to divide into those mentioning hallucinogens derived from amphetamine and those derived from ergot. Conversations that mentioned synthetic hallucinogens tended not to mention plant hallucinogens. Conversations that mention bath salts commonly mention sedative-hypnotics or nootropics while more canonical stimulants are discussed with plant hallucinogens and psychiatric medications. All types of substances are frequently compared to MDMA, DMT, cocaine, or atropine when trying to describe their effects. Our results provide the largest analysis to date of online descriptions of patterns of polysubstance use and further demonstrate the utility of social media in learning about trends in substance use. We anticipate this work to lead to a more detailed analysis of the knowledge contained online about the patterns of usage and effects of novel psychoactive substances.
Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein-protein interactome, we show the existence of six distinct classes of drug-drug-disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.
Background: Enterovirus infections affect people around the world, causing a range of illnesses, from mild fevers to severe, potentially fatal conditions. There are no approved treatments for enterovirus infections. Methods: We have tested our library of broad-spectrum antiviral agents (BSAs) against echovirus 1 (EV1) in human adenocarcinoma alveolar basal epithelial A549 cells. We also tested combinations of the most active compounds against EV1 in A549 and human immortalized retinal pigment epithelium RPE cells. Results: We confirmed anti-enteroviral activities of pleconaril, rupintrivir, cycloheximide, vemurafenib, remdesivir, emetine, and anisomycin and identified novel synergistic rupintrivir-vemurafenib, vemurafenib-pleconaril and rupintrivir-pleconaril combinations against EV1 infection. Conclusions: Because rupintrivir, vemurafenib, and pleconaril require lower concentrations to inhibit enterovirus replication in vitro when combined, their cocktails may have fewer side effects in vivo and, therefore, should be further explored in preclinical and clinical trials against EV1 and other enterovirus infections.
Combination chemotherapy is the leading clinical option for cancer treatment. The current approach to designing drug combinations includes in vitro optimization to maximize drug cytotoxicity and/or synergistic drug interactions. However, in vivo translatability of drug combinations is complicated by the disparities in drug pharmacokinetics and activity. In vitro cellular assays also fail to represent the immune response that can be amplified by chemotherapy when dosed appropriately. Using three common chemotherapeutic drugs, gemcitabine (GEM), irinotecan (IRIN), and a prodrug form of 5-flurouracil (5FURW), paired with another common drug and immunogenic cell death inducing agent, doxorubicin (DOX), we sought to determine the in vitro parameters that predict the in vivo outcomes of drug combinations in the highly aggressive orthotopic 4T1 murine breast cancer model. With liposomal encapsulation of each drug pair, we enabled uniform drug pharmacokinetics across the drug combinations, thus allowing us to study the inherent benefits of the drug pairs and compare them to DOX liposomes representative of DOXIL®. Surprisingly, the Hill coefficient (HC) of the in vitro dose-response Hill equation provided a better prediction of in vivo efficacy than drug IC50 or combination index. GEM/DOX liposomes exhibited a high HC in vitro and an increase in M1/M2 macrophage ratio in vivo. Hence, GEM/DOX liposomes were further investigated in a long-term survival study and compared against doxorubicin liposomes and gemcitabine liposomes. The GEM/DOX liposome-treated group had the longest median survival time, double that of the DOX liposome-treated group and 3.4-fold greater than that of the untreated controls. Our studies outline the development of a more efficacious formulation than clinically representative liposomal doxorubicin for breast cancer treatment and presents a novel strategy for designing cancer drug combinations.
Infectious diseases account for nearly one fifth of the worldwide death toll every year. The continuous increase of drug-resistant pathogens is a big challenge for treatment of infectious diseases. In addition, outbreaks of infections and new pathogens are potential threats to public health. Lack of effective treatments for drug-resistant bacteria and recent outbreaks of Ebola and Zika viral infections have become a global public health concern. The number of newly approved antibiotics has decreased significantly in the last two decades compared with previous decades. In parallel with this, is an increase in the number of drug-resistant bacteria. For these threats and challenges to be countered, new strategies and technology platforms are critically needed. Drug repurposing has emerged as an alternative approach for rapid identification of effective therapeutics to treat the infectious diseases. For treatment of severe infections, synergistic drug combinations using approved drugs identified from drug repurposing screens is a useful option which may overcome the problem of weak activity of individual drugs. Collaborative efforts including government, academic researchers and private drug industry can facilitate the translational research to produce more effective new therapeutic agents such as narrow spectrum antibiotics against drug-resistant bacteria for these global challenges.
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.
Combination therapies have become a standard for the treatment for HIV and hepatitis C virus (HCV) infections. They are advantageous over monotherapies due to better efficacy, reduced toxicity, as well as the ability to prevent the development of resistant viral strains and to treat viral co-infections. Here, we identify new synergistic combinations against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), echovirus 1 (EV1), hepatitis C virus (HCV) and human immunodeficiency virus 1 (HIV-1) in vitro. We observed synergistic activity of nelfinavir with convalescent serum and with purified neutralizing antibody 23G7 against SARS-CoV-2 in human lung epithelial Calu-3 cells. We also demonstrated synergistic activity of nelfinavir with EIDD-2801 or remdesivir in Calu-3 cells. In addition, we showed synergistic activity of vemurafenib with emetine, homoharringtonine, anisomycin, or cycloheximide against EV1 infection in human lung epithelial A549 cells. We also found that combinations of sofosbuvir with brequinar or niclosamide are synergistic against HCV infection in hepatocyte-derived Huh-7.5 cells, and that combinations of monensin with lamivudine or tenofovir are synergistic against HIV-1 infection in human cervical TZM-bl cells. These results indicate that synergy is achieved when a virus-directed antiviral is combined with another virus- or host-directed agent. Finally, we present an online resource that summarizes novel and known antiviral drug combinations and their developmental status.
Chordoma is a devastating rare cancer that affects one in a million people. With a mean-survival of just 6 years and no approved medicines, the primary treatments are surgery and radiation. In order to speed new medicines to chordoma patients, a drug repurposing strategy represents an attractive approach. Drugs that have already advanced through human clinical safety trials have the potential to be approved more quickly than de novo discovered medicines on new targets. We have taken two strategies to enable this: (1) generated and validated machine learning models of chordoma inhibition and screened compounds of interest in vitro. (2) Tested combinations of approved kinase inhibitors already being individually evaluated for chordoma. Several published studies of compounds screened against chordoma cell lines were used to generate Bayesian Machine learning models which were then used to score compounds selected from the NIH NCATS industry-provided assets. Out of these compounds, the mTOR inhibitor AZD2014, was the most potent against chordoma cell lines (IC50 0.35 µM U-CH1 and 0.61 µM U-CH2). Several studies have shown the importance of the mTOR signaling pathway in chordoma and suggest it as a promising avenue for targeted therapy. Additionally, two currently FDA approved drugs, afatinib and palbociclib (EGFR and CDK4/6 inhibitors, respectively) demonstrated synergy in vitro (CI50 = 0.43) while AZD2014 and afatanib also showed synergy (CI50 = 0.41) against a chordoma cell in vitro. These findings may be of interest clinically, and this in vitro- and in silico approach could also be applied to other rare cancers.
Drug combinations for the treatment of leishmaniasis represent a promising and challenging chemotherapeutic strategy that has recently been implemented in different endemic areas. However, the vast majority of studies undertaken to date have ignored the potential risk that Leishmania parasites could develop resistance to the different drugs used in such combinations. As a result, this study was designed to elucidate the ability of Leishmania donovani to develop experimental resistance to anti-leishmanial drug combinations. The induction of resistance to amphotericin B/miltefosine, amphotericin B/paromomycin, amphotericin B/Sb(III), miltefosine/paromomycin, and Sb(III)/paromomycin was determined using a step-wise adaptation process to increasing drug concentrations. Intracellular amastigotes resistant to these drug combinations were obtained from resistant L. donovani promastigote forms, and the thiol and ATP levels and the mitochondrial membrane potential of the resistant lines were analysed. Resistance to drug combinations was obtained after 10 weeks and remained in the intracellular amastigotes. Additionally, this resistance proved to be unstable. More importantly, we observed that promastigotes/amastigotes resistant to one drug combination showed a marked cross-resistant profile to other anti-leishmanial drugs. Additionally, the thiol levels increased in resistant lines that remained protected against the drug-induced loss of ATP and mitochondrial membrane potential. We have therefore demonstrated that different resistance patterns can be obtained in L. donovani depending upon the drug combinations used. Resistance to the combinations miltefosine/paromomycin and Sb(III)/paromomycin is easily obtained experimentally. These results have been validated in intracellular amastigotes, and have important relevance for ensuring the long-term efficacy of drug combinations.
A promising alternative to address the problem of acquired drug resistance is to rely on combination therapies. Identification of the right combinations is often accomplished through trial and error, a labor and resource intensive process whose scale quickly escalates as more drugs can be combined. To address this problem, we present a broad computational approach for predicting synergistic combinations using easily obtainable single drug efficacy, no detailed mechanistic understanding of drug function, and limited drug combination testing. When applied to mutant BRAF melanoma, we found that our approach exhibited significant predictive power. Additionally, we validated previously untested synergy predictions involving anticancer molecules. As additional large combinatorial screens become available, this methodology could prove to be impactful for identification of drug synergy in context of other types of cancers.
Burkitt lymphoma (BL) is a highly aggressive B-cell lymphoma associated with MYC translocation. Here, we describe drug response profiling of 42 blood cancer cell lines including 17 BL to 32 drugs targeting key cancer pathways and provide a systematic study of drug combinations in BL cell lines. Based on drug response, we identified cell line specific sensitivities, i.e. to venetoclax driven by BCL2 overexpression and partitioned subsets of BL driven by response to kinase inhibitors. In the combination screen, including BET, BTK and PI3K inhibitors, we identified synergistic combinations of PI3K and BTK inhibition with drugs targeting Akt, mTOR, BET and doxorubicin. A detailed comparison of PI3K and BTKi combinations identified subtle differences, in line with convergent pathway activity. Most synergistic combinations were identified for the BET inhibitor OTX015, which showed synergistic effects for 41% of combinations including inhibitors of PI3K/AKT/mTOR signalling. The strongest synergy was observed for the combination of the CDK 2/7/9 inhibitor SNS032 and OTX015. Our data provide a landscape of drug combination effects in BL and suggest that targeting CDK and BET could provide a novel vulnerability of BL.
Contemporary chemotherapeutic treatments incorporate the use of several agents in combination. However, selecting the most appropriate drugs for such therapy is not necessarily an easy or straightforward task. Here, we describe a targeted approach that can facilitate the reliable selection of chemotherapeutic drug combinations through the interrogation of drug-resistance gene networks. Our method employed single-cell eukaryote fission yeast (Schizosaccharomyces pombe) as a model of proliferating cells to delineate a drug resistance gene network using a synthetic lethality workflow. Using the results of a previous unbiased screen, we assessed the genetic overlap of doxorubicin with six other drugs harboring varied mechanisms of action. Using this fission yeast model, drug-specific ontological sub-classifications were identified through the computation of relative hypersensitivities. We found that human gastric adenocarcinoma cells can be sensitized to doxorubicin by concomitant treatment with cisplatin, an intra-DNA strand crosslinking agent, and suberoylanilide hydroxamic acid, a histone deacetylase inhibitor. Our findings point to the utility of fission yeast as a model and the differential targeting of a conserved gene interaction network when screening for successful chemotherapeutic drug combinations for human cells.
Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper's transparent peer review process is included in the Supplemental Information.
Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico.
During polychemotherapy, cytotoxic drugs are given in combinations to enhance their anti-tumor effectiveness. For most drug combinations, underlying signaling mechanisms responsible for positive drug-drug interactions remain elusive. Here, we prove a decisive role for the Bcl-2 family member NOXA to mediate cell death by certain drug combinations, even if drugs were combined which acted independently from NOXA, when given alone. In proof-of-principle studies, betulinic acid, doxorubicin and vincristine induced cell death in a p53- and NOXA-independent pathway involving mitochondrial pore formation, release of cytochrome c and caspase activation. In contrast, when betulinic acid was combined with either doxorubicine or vincristine, cell death signaling changed considerably; the drug combinations clearly depended on both p53 and NOXA. Similarly and of high clinical relevance, in patient-derived childhood acute leukemia samples the drug combinations, but not the single drugs depended on p53 and NOXA, as shown by RNA interference studies in patient-derived cells. Our data emphasize that NOXA represents an important target molecule for combinations of drugs that alone do not target NOXA. NOXA might have a special role in regulating apoptosis sensitivity in the complex interplay of polychemotherapy. Deciphering the differences in signaling of single drugs and drug combinations might enable designing highly effective novel polychemotherapy regimens.
Combination therapy is increasingly central to modern medicine. Yet reliable analysis of combination studies remains an open challenge. Previous work suggests that common methods of combination analysis are too susceptible to noise to support robust scientific conclusions. In this paper, we use simulated and real-world combination datasets to demonstrate that traditional index methods are unstable and biased by pharmacological and experimental conditions, whereas response-surface approaches such as the BRAID method are more consistent and unbiased. Using a publicly-available data set, we show that BRAID more accurately captures variations in compound mechanism of action, and is therefore better able to discriminate between synergistic, antagonistic, and additive interactions. Finally, we applied BRAID analysis to identify a clear pattern of consistently enhanced AKT sensitivity in a subset of cancer cell lines, and a far richer array of PARP inhibitor combination therapies for BRCA1-deficient cancers than would be identified by traditional synergy analysis.
Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled ~8,000 combinations of 65 antibacterial drugs against the model species Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface ( https://apps.embl.de/combact/ ).
Tuberculosis (TB) is a leading global cause of mortality owing to an infectious agent, accounting for almost one-third of antimicrobial resistance (AMR) deaths annually. We aimed to identify synergistic anti-TB drug combinations with the capacity to restore therapeutic efficacy against drug-resistant mutants of the causative agent, Mycobacterium tuberculosis We investigated combinations containing the known translational inhibitors, spectinomycin (SPT) and fusidic acid (FA), or the phenothiazine, chlorpromazine (CPZ), which disrupts mycobacterial energy metabolism. Potentiation of whole-cell drug efficacy was observed in SPT-CPZ combinations. This effect was lost against an M. tuberculosis mutant lacking the major facilitator superfamily (MFS) efflux pump, Rv1258c. Notably, the SPT-CPZ combination partially restored SPT efficacy against an SPT-resistant mutant carrying a g1379t point mutation in rrs, encoding the mycobacterial 16S ribosomal RNA. Combinations of SPT with FA, which targets the mycobacterial elongation factor G, exhibited potentiating activity against wild-type M. tuberculosis Moreover, this combination produced a modest potentiating effect against both FA-monoresistant and SPT-monoresistant mutants. Finally, combining SPT with the frontline anti-TB agents, rifampicin (RIF) and isoniazid, resulted in enhanced activity in vitro and ex vivo against both drug-susceptible M. tuberculosis and a RIF-monoresistant rpoB S531L mutant.These results support the utility of novel potentiating drug combinations in restoring antibiotic susceptibility of M. tuberculosis strains carrying genetic resistance to any one of the partner compounds.
Drug combinations are a promising strategy to overcome the compensatory mechanisms and unwanted off-target effects that limit the utility of many potential drugs. However, enthusiasm for this approach is tempered by concerns that the therapeutic synergy of a combination will be accompanied by synergistic side effects. Using large scale simulations of bacterial metabolism and 94,110 multi-dose experiments relevant to diverse diseases, we provide evidence that synergistic drug combinations are generally more specific to particular cellular contexts than are single agent activities. We highlight six combinations whose selective synergy depends on multitarget drug activity. For one anti-inflammatory example, we show how such selectivity is achieved through differential expression of the drugs' targets in cell types associated with therapeutic, but not toxic, effects and validate its therapeutic relevance in a rat model of asthma. The context specificity of synergistic combinations creates many opportunities for therapeutically relevant selectivity and enables improved control of complex biological systems.
Drug resistance represents one of the main problems for the use of chemotherapy to treat leishmaniasis. Additionally, it could provide some advantages to Leishmania parasites, such as a higher capacity to survive in stress conditions. In this work, in mixed populations of Leishmania donovani parasites, we have analyzed whether experimentally resistant lines to one or two combined anti-leishmanial drugs better support the stress conditions than a susceptible line expressing luciferase (Luc line). In the absence of stress, none of the Leishmania lines showed growth advantage relative to the other when mixed at a 1:1 parasite ratio. However, when promastigotes from resistant lines and the Luc line were mixed and exposed to different stresses, we observed that the resistant lines are more tolerant of different stress conditions: nutrient starvation and heat shock-pH stress. Further to this, we observed that intracellular amastigotes from resistant lines present a higher capacity to survive inside the macrophages than those of the control line. These results suggest that resistant parasites acquire an overall fitness increase and that resistance to drug combinations presents significant differences in their fitness capacity versus single-drug resistant parasites, particularly in intracellular amastigotes. These results contribute to the assessment of the possible impact of drug resistance on leishmaniasis control programs.
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