Prediction and identification of synergistic compound combinations against pancreatic cancer cells.
Ontology highlight
ABSTRACT:
SUBMITTER: KalantarMotamedi Y
PROVIDER: S-EPMC8456050 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Immunostimulatory monoclonal antibodies can be given in combinations, hence modulating the activity of 2 or more receptors of the immune system. Some of these combinations have been shown to synergize at the elicitation of therapeutically relevant immune responses in transgenic mice developing spontaneous, oncogene-driven tumors, including multifocal hepatocellular carcinomas expressing ovalbumin as a surrogate tumor-associated antigen.
| S-EPMC4091451 | biostudies-literature
Project description:Platinum-taxane combination chemotherapy still represents the standard of care for advanced non-small cell lung cancer (NSCLC) with no targetable driver mutations. However, the efficacy of these drugs has plateaued at 10-14 months primarily due to dose-limiting toxicity, chemoresistance, and metastasis. Here, we explored the effects of withaferin A (WFA) alone and in combination with paclitaxel (PAC) on the growth, proliferation, migration, and invasion of human NSCLC cells. We show that the sensitivity of H1299 and A549 cells to concomitant treatment with PAC and WFA was greater than that of either PAC or WFA alone. Using the combination index and dose-reduction index, we demonstrated that various combinations (1:40, 1:20, 1:10) of PAC to WFA, respectively, were highly synergistic. In addition, PAC+WFA co-treatment synergistically inhibited colony formation, migration, invasion and increased the induction of apoptosis in H1299 and A549 cells. Interestingly, the synergism of PAC and WFA was not schedule-dependent but was enhanced when cells were pretreated with WFA indicating a chemo-sensitizing effect. Importantly, WFA was active against both PAC-sensitive (TS-A549) and PAC-resistant (TR-A549) cells both in vitro and in vivo. Mechanistically, WFA inhibits the proliferation of NSCLC cells via thiol oxidation. The effects of WFA were inhibited in the presence of N-acetyl cysteine and other thiol donors. Taken together, our results demonstrate the efficacy of WFA alone or alongside PAC on NSCLC cells and provide a strong rationale for further detailed testing in clinically relevant models for the development of PAC+WFA combination as an alternative therapeutic strategy for advanced NSCLC.
| S-EPMC7185067 | biostudies-literature
Project description:Compared with other breast cancer subtypes, triple-negative breast cancer (TNBC) is associated with relatively poor outcomes due to its metastatic propensity, frequent failure to respond to chemotherapy, and lack of alternative, targeted treatment options, despite decades of major research efforts. Our studies sought to identify promising targeted therapeutic candidates for TNBC through in vitro screening of 1,363 drugs in patient-derived xenograft (PDX) models. Using this approach, we generated a dataset that can be used to assess and compare responses of various breast cancer PDXs to many different drugs. Through a series of further drug screening assays and two-drug combination testing, we identified that the combination of afatinib (epidermal growth factor receptor (EGFR) inhibitor) and YM155 (inhibitor of baculoviral inhibitor of apoptosis repeat-containing 5 (BIRC5; survivin) expression) is synergistically cytotoxic across multiple models of basal-like TNBC and reduces PDX mammary tumor growth in vivo. We found that YM155 reduces EGFR expression in TNBC cells, shedding light on its potential mechanism of synergism with afatinib. Both EGFR and BIRC5 are highly expressed in basal-like PDXs, cell lines, and patients, and high expression of both genes reduces metastasis-free survival, suggesting that co-targeting of these proteins holds promise for potential clinical success in TNBC.
| S-EPMC6992640 | 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:Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.
| S-EPMC8112211 | biostudies-literature
Project description:Resistance to colistin, a polypeptide drug used as an agent of last resort for the treatment of infections caused by multidrug-resistant (MDR) and extensively drug-resistant (XDR) Gram-negative bacteria, including carbapenem-resistant Enterobacteriaceae (CRE), severely limits treatment options and may even transform an XDR organism into one that is pan-resistant. We investigated the synergistic activity of colistin in combination with 19 antibiotics against a collection of 20 colistin-resistant Enterobacteriaceae isolates, 15 of which were also CRE. All combinations were tested against all strains using an inkjet printer-assisted digital dispensing checkerboard array, and the activities of those that demonstrated synergy by this method were evaluated against a single isolate in a time-kill synergy study. Eighteen of 19 combinations demonstrated synergy against two or more isolates, and the 4 most highly synergistic combinations (colistin combined with linezolid, rifampin, azithromycin, and fusidic acid) were synergistic against ≥90% of strains. Sixteen of 18 combinations (88.9%) that were synergistic in the checkerboard array were also synergistic in a time-kill study. Our findings demonstrate that colistin in combination with a range of antibiotics, particularly protein and RNA synthesis inhibitors, exhibits synergy against colistin-resistant strains, suggesting that colistin may exert a subinhibitory permeabilizing effect on the Gram-negative bacterial outer membrane even in isolates that are resistant to it. These findings suggest that colistin combination therapy may have promise as a treatment approach for patients infected with colistin-resistant XDR Gram-negative pathogens.
| S-EPMC6153801 | biostudies-other
Project description:Background:A need to develop therapeutics to treat Ebola virus disease patients in remote and resource-challenged settings remains in the wake of the 2013-2016 epidemic in West Africa. Toward this goal, we screened drugs under consideration as treatment options and other drugs of interest, most being small molecules approved by the Food and Drug Administration. Drugs demonstrating in vitro antiviral activity were advanced for evaluation in combinations because of advantages often provided by drug cocktails. Methods:Drugs were screened for blockade of Ebola virus infection in cultured cells. Twelve drugs were tested in all (78 pair-wise) combinations, and 3 were tested in a subset of combinations. Results:Multiple synergistic drug pairs emerged, with the majority comprising 2 entry inhibitors. For the pairs of entry inhibitors studied, synergy was demonstrated at the level of virus entry into host cells. Highly synergistic pairs included aripiprazole/piperacetazine, sertraline/toremifene, sertraline/bepridil, and amodiaquine/clomiphene. Conclusions:Our study shows the feasibility of identifying pairs of approved drugs that synergistically block Ebola virus infection in cell cultures. We discuss our findings in terms of the theoretic ability of these or alternate combinations to reach therapeutic levels. Future research will assess selected combinations in small-animal models of Ebola virus disease.
| S-EPMC6249579 | biostudies-literature
Project description:The parasite Plasmodium falciparum is the most lethal species of Plasmodium to cause serious malaria infection in humans, and with resistance developing rapidly novel treatment modalities are currently being sought, one of which being combinations of existing compounds. The discovery of combinations of antimalarial drugs that act synergistically with one another is hence of great importance; however an exhaustive experimental screen of large drug space in a pairwise manner is not an option. In this study we apply our machine learning approach, Combination Synergy Estimation (CoSynE), which can predict novel synergistic drug interactions using only prior experimental combination screening data and knowledge of compound molecular structures, to a dataset of 1,540 antimalarial drug combinations in which 22.2% were synergistic. Cross validation of our model showed that synergistic CoSynE predictions are enriched 2.74 × compared to random selection when both compounds in a predicted combination are known from other combinations among the training data, 2.36 × when only one compound is known from the training data, and 1.5 × for entirely novel combinations. We prospectively validated our model by making predictions for 185 combinations of 23 entirely novel compounds. CoSynE predicted 20 combinations to be synergistic, which was experimentally validated for nine of them (45%), corresponding to an enrichment of 1.70 × compared to random selection from this prospective data set. Such enrichment corresponds to a 41% reduction in experimental effort. Interestingly, we found that pairwise screening of the compounds CoSynE individually predicted to be synergistic would result in an enrichment of 1.36 × compared to random selection, indicating that synergy among compound combinations is not a random event. The nine novel and correctly predicted synergistic compound combinations mainly (where sufficient bioactivity information is available) consist of efflux or transporter inhibitors (such as hydroxyzine), combined with compounds exhibiting antimalarial activity alone (such as sorafenib, apicidin, or dihydroergotamine). However, not all compound synergies could be rationalized easily in this way. Overall, this study highlights the potential for predictive modeling to expedite the discovery of novel drug combinations in fight against antimalarial resistance, while the underlying approach is also generally applicable.
| S-EPMC6176478 | biostudies-literature
Project description:We propose the use of iron-doped
| S-EPMC9953328 | biostudies-literature
Project description:Pancreatic cancer is a lethal disease with a dreadful 5-year survival rate of only 5%. In spite of several treatment options, the prognosis still remains extremely poor. Therefore, novel therapy strategies with combinations of drugs are urgently required to combat this fatal disease. Triptolide (TPL) and celastrol (CL), two main compounds in traditional Chinese medicine isolated from Thunder God Vine, have a broad range of bioactivities including anticancer activity. Silk fibroin (SF), a naturally occurring protein with several unique properties, is an ideal carrier material. In this study, we prepared TPL and CL loaded silk fibroin nanoparticles (TPL-SFNPs and CL-SFNPs) by a modified desolvation method and evaluated their synergistic effects against human pancreatic cancer cells. Both SFNPs were characterized for particle size and zeta potential. The entrapment efficiency, drug loading, and drug release profiles were evaluated by HPLC. The cytotoxicity and synergistic effect of SFNPs were investigated in MIA PaCa-2 and PANC-1 human pancreatic cells. The results showed that the particle sizes of TPL-SFNPs and CL-SFNPs were 166.4 ± 4.6 nm and 170.4 ± 2.3 nm, with a mean zeta potential -27.2 ± 2.0 mV and -25.5 ± 2.57 mV, respectively. TPL-SFNPs and CL-SFNPs have a drug loading of 57.0 ± 4.7 μg mg-1 and 63.5 ± 3.8 μg mg-1 along with an encapsulation efficiency of 81.8 ± 2.8% and 87.0 ± 5.1%, respectively. Drug release studies revealed that a rapid release of the drugs from SFNPs was observed at pH 4.5 (lysosomal pH) and a delayed release was observed at pH 7.4 (plasma pH). TPL-SFNPs (IC50 3.80 and 4.75 nM) and CL-SFNPs (IC50 0.38 and 0.64 μM) were 2-3 fold more potent against MIA PaCa-2 and PANC-1 cells than free TPL (IC50 11.25 and 11.58 nM) and CL (IC50 0.84 and 1.23 μM). Furthermore, co-treatment with TPL-SFNPs and CL-SFNPs increased the growth inhibition of the same cells significantly in comparison with TPL-SFNPs or CL-SFNPs alone. Almost all combination index (CI) values, calculated using the CompuSyn software, were <1, suggesting that the growth inhibition effect of TPL-SFNPs in combination with CL-SFNPs was synergistic rather than additive, further suggesting that this novel combination may offer a potential treatment for pancreatic cancer.
| S-EPMC5648537 | biostudies-literature