A Computational Approach for Identifying Synergistic Drug Combinations.
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ABSTRACT:
SUBMITTER: Gayvert KM
PROVIDER: S-EPMC5234777 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Nearly half of the world's population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current resistance mechanisms. In the work presented here, a combined transcriptional drug repositioning/discovery and machine learning approach is proposed. METHODS:The integrated approach utilizes gene expression data from patient-derived samples, in combination with large-scale anti-malarial combination screening data, to predict synergistic compound combinations for three Plasmodium falciparum strains (3D7, DD2 and HB3). Both single compounds and combinations predicted to be active were prospectively tested in experiment. RESULTS:One of the predicted single agents, apicidin, was active with the AC50 values of 74.9, 84.1 and 74.9 nM in 3D7, DD2 and HB3 P. falciparum strains while its maximal safe plasma concentration in human is 547.6?±?136.6 nM. Apicidin at the safe dose of 500 nM kills on average 97% of the parasite. The synergy prediction algorithm exhibited overall precision and recall of 83.5 and 65.1% for mild-to-strong, 48.8 and 75.5% for moderate-to-strong and 12.0 and 62.7% for strong synergies. Some of the prospectively predicted combinations, such as tacrolimus-hydroxyzine and raloxifene-thioridazine, exhibited significant synergy across the three P. falciparum strains included in the study. CONCLUSIONS:Systematic approaches can play an important role in accelerating discovering novel combinational therapies for malaria as it enables selecting novel synergistic compound pairs in a more informed and cost-effective manner.
| S-EPMC5896032 | biostudies-literature
Project description:We examined the mode-of-action of synergistic drug combinations by microarray analysis. We focused on the drug combination of capsaicin and mitoxantrone, which were the top predicted drug pair identified by SyndrumNET. Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses. The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML), and it outperformed the previous methods in terms of high accuracy. We performed in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibited synergistic anticancer effects. Our mode-of-action analysis also revealed that the drug synergy of the top predicted combination of capsaicin and mitoxantrone was due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway. The proposed method is expected to be useful for various complex diseases.
2024-05-06 | GSE254052 | GEO
Project description:Glioblastoma (GBM) is a highly aggressive and heterogeneous form of brain cancer. Genotypic and phenotypic heterogeneity drives drug resistance and tumor recurrence. Combination chemotherapy could overcome drug resistance; however, GBM's location behind the blood-brain barrier severely limits chemotherapeutic options. Interstitial therapy, delivery of chemotherapy locally to the tumor site, via a biodegradable polymer implant can overcome the blood-brain barrier and increase the range of drugs available for therapy. Ideal drug candidates for interstitial therapy are those that are potent against GBM and work in combination with both standard-of-care therapy and new precision medicine targets. Herein we evaluated paclitaxel for interstitial therapy, investigating the effect of combination with both temozolomide, a clinical standard-of-care chemotherapy for GBM, and everolimus, a mammalian target of rapamycin (mTOR) inhibitor that modulates aberrant signaling present in >80% of GBM patients. Tested against a panel of GBM cell lines in vitro, paclitaxel was found to be effective at nanomolar concentrations, complement therapy with temozolomide, and synergize strongly with everolimus. The strong synergism seen with paclitaxel and everolimus was then explored in vivo. Paclitaxel and everolimus were separately formulated into fibrous scaffolds composed of acetalated dextran, a biodegradable polymer with tunable degradation rates, for implantation in the brain. Acetalated dextran degradation rates were tailored to attain matching release kinetics (~3% per day) of both paclitaxel and everolimus to maintain a fixed combination ratio of the two drugs. Combination interstitial therapy of both paclitaxel and everolimus significantly reduced GBM growth and improved progression free survival in two clinically relevant orthotopic models of GBM resection and recurrence. This work illustrates the advantages of synchronized interstitial therapy of paclitaxel and everolimus for post-surgical tumor control of GBM.
| S-EPMC7453575 | biostudies-literature
Project description:An imaged-based profiling and analysis system was developed to predict clinically effective synergistic drug combinations that could accelerate the identification of effective multi-drug therapies for the treatment of triple-negative breast cancer and other challenging malignancies. The identification of effective drug combinations for the treatment of triple-negative breast cancer (TNBC) was achieved by integrating high-content screening, computational analysis, and experimental biology. The approach was based on altered cellular phenotypes induced by 55 FDA-approved drugs and biologically active compounds, acquired using fluorescence microscopy and retained in multivariate compound profiles. Dissimilarities between compound profiles guided the identification of 5 combinations, which were assessed for qualitative interaction on TNBC cell growth. The combination of the microtubule-targeting drug vinblastine with KSP/Eg5 motor protein inhibitors monastrol or ispinesib showed potent synergism in 3 independent TNBC cell lines, which was not substantiated in normal fibroblasts. The synergistic interaction was mediated by an increase in mitotic arrest with cells demonstrating typical ispinesib-induced monopolar mitotic spindles, which translated into enhanced apoptosis induction. The antitumour activity of the combination vinblastine/ispinesib was confirmed in an orthotopic mouse model of TNBC. Compared to single drug treatment, combination treatment significantly reduced tumour growth without causing increased toxicity. Image-based profiling and analysis led to the rapid discovery of a drug combination effective against TNBC in vitro and in vivo, and has the potential to lead to the development of new therapeutic options in other hard-to-treat cancers.
| S-EPMC4253311 | biostudies-literature
Project description: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.
| S-EPMC9505890 | biostudies-literature
Project description:MotivationDrug combination therapy shows significant advantages over monotherapy in cancer treatment. Since the combinational space is difficult to be traversed experimentally, identifying novel synergistic drug combinations based on computational methods has become a powerful tool for pre-screening. Among them, methods based on deep learning have far outperformed other methods. However, most deep learning-based methods are unstable and will give inconsistent predictions even by simply changing the input order of drugs. In addition, the insufficient experimental data of drug combination screening limits the generalization ability of existing models. These problems prevent the deep learning-based models from being in service.ResultsIn this article, we propose CGMS to address the above problems. CGMS models a drug combination and a cell line as a heterogeneous complete graph, and generates the whole-graph embedding to characterize their interaction by leveraging the heterogeneous graph attention network. Based on the whole-graph embedding, CGMS can make a stable, order-independent prediction. To enhance the generalization ability of CGMS, we apply the multi-task learning technique to train the model on drug synergy prediction task and drug sensitivity prediction task simultaneously. We compare CGMS's generalization ability with six state-of-the-art methods on a public dataset, and CGMS significantly outperforms other methods in the leave-drug combination-out scenario, as well as in the leave-cell line-out and leave-drug-out scenarios. We further present the benefit of eliminating the order dependency and the discrimination power of whole-graph embeddings, interpret the rationality of the attention mechanism, and verify the contribution of multi-task learning.Availability and implementationThe code of CGMS is available via https://github.com/TOJSSE-iData/CGMS.
| S-EPMC10256571 | biostudies-literature
Project description: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.
| S-EPMC7347197 | biostudies-literature
Project description: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.Linked articlesThis article is part of a themed section on Inventing New Therapies Without Reinventing the Wheel: The Power of Drug Repurposing. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.2/issuetoc.
| S-EPMC5758396 | biostudies-literature
Project description:There is compelling evidence that synergistic drug combinations have become promising strategies for combating complex diseases, and they have evident predominance comparing to traditional one drug - one disease approaches. In this paper, we develop a computational method, namely SyFFM, that takes pharmacological data into consideration and applies field-aware factorization machines to analyze and predict potential synergistic drug combinations. Firstly, features of drug pairs are constructed based on associations between drugs and target, and enzymes, and indication areas. Then, the synergistic scores of drug combinations are obtained by implementing field-aware factorization machines on latent vector space of these features. Finally, synergistic combinations can be predicted by introducing a threshold. We applied SyFFM to predict pairwise synergistic combinations and three-drug synergistic combinations, and the performance is good in terms of cross-validation. Besides, more than 90% combinations of the top ranked predictions are proved by literature and the analysis of parameters in model shows that our method can help to investigate and explain synergistic mechanisms underlying combinatorial therapy.
| S-EPMC6370570 | biostudies-literature
Project description: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.
| S-EPMC7395084 | biostudies-literature