The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.
Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs. Of the 12 pairs predicted to be synergistic, 10 were experimentally validated, corresponding to a 2.8-fold enrichment. Having thus validated our methodology, we produced a compendium of interaction predictions for all pairwise combinations among 100 antibiotics. Our methodology can make reliable antibiotic interaction predictions for any antibiotic pair within the applicability domain of the model since it solely requires chemical structures as an input.
a b s t r a c tHepatocellular carcinoma (HCC) is the fifth most common malignant tumor worldwide, and is the third most common cause of cancer related death. Constitutive activation of NF-jB is the underlying mechanism behind tumorigenesis and this protein regulates the expression of genes involved in proliferation, survival, drug resistance, angiogenesis and metastasis. The design of inhibitors which suppress NF-jB activation is therefore of great therapeutic importance in the treatment of HCC. In this study, we investigated the effect of newly synthesized coumarin derivatives against HCC cells, and identified (7-Carbethoxyamino-2-oxo-2H-chromen-4-yl)methylpyrrolidine-1 carbodithioate (CPP) as lead compound. Further, we evaluated the effect of CPP on the DNA binding ability of NF-jB, CXCL12-induced cell migration and invasion, and the regulated gene products in HCC cells. We found that CPP induced cytotoxicity in three HCC cells in a time and dose dependent manner, and suppressed the DNA binding ability of NF-jB. CPP significantly decreased the CXCL12-induced cell migration and invasion. More evidently, CPP inhibits the expression of NF-jB targeted genes such as cyclin D1, Bcl-2, survivin, MMP12 and C-Myc. Furthermore, the molecular docking analysis suggested that CPP interacts with the p50 binding domain of the p65 subunit, scoring best among the 26 docked coumarin derivatives of this study. Thus, we are reporting CPP as a potent inhibitor of the pro-inflammatory pathway in Hepatocellular carcinoma.Ó 2014 Elsevier Ltd. All rights reserved.Hepatocellular carcinoma (HCC) is the most common form of liver cancer, and is the third most common cause of cancer related mortality. 1 Obesity, hepatitis B or hepatitis C virus infection, consumption of aflatoxin B1, and alcoholic hepatitis are the major risk factors of HCC. 2 The majority of patients with HCC are diagnosed at a late stage of the disease, with therapy limited to only liver transplantation and surgical liver sectioning. Early detection of HCC may contribute to the strengthening of prognosis. 3,4 Nuclear factor kappa B (NF-jB) is an inducible transcription factor present ubiquitously in the cytoplasm of almost all mammalian cells, and was identified by Baltimore and colleagues in 1986. 5 In an unstimulated cell, NF-jB is associated with IjB (inhibitory jB), and resides in the cytoplasm. 6 Different ligands acting upon the receptor result in a cascade of upstream kinases, finally leading to phosphorylation and ubiquitylation of IjB, which is targeted for degradation. NFjB translocates into nucleus and regulates transcription of the genes involved in inflammation. 7 NF-jB is referred to as a double-edged sword by researchers because of its critical role in proper functioning of the immune system, and the possibility that oncogenesis may arise from the slightest change in regulation. 8 Several anti-apoptotic, angiogenic, inflammatory, metastasis-related, and http://dx
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.
The anti-apoptotic protein Bcl-2 is a well-known and attractive therapeutic target for cancer. In the present study the solution-phase T3P-DMSO mediated efficient synthesis of 2-amino-chromene-3-carbonitriles from alcohols, malanonitrile and phenols is reported. These novel 2-amino-chromene-3-carbonitriles showed cytotoxicity in human acute myeloid leukemia (AML) cell lines. Compound 4g was found to be the most bioactive, decreasing growth and increasing apoptosis of AML cells. Moreover, compound 4g (at a concentration of 5 µM) increased the G2/M and sub-G1 (apoptosis) phases of AML cells. The AML cells treated with compound 4g exhibited decreased levels of Bcl-2 and increased levels of caspase-9. In silico molecular interaction analysis showed that compound 4g shared a similar global binding motif with navitoclax (another small molecule that binds Bcl-2), however compound 4g occupies a smaller volume within the P2 hot spot of Bcl-2. The intermolecular π-stacking interaction, direct electrostatic interactions, and docking energy predicted for 4g in complex with Bcl-2 suggest a strong affinity of the complex, rendering 4g as a promising Bcl-2 inhibitor for evaluation as a new anticancer agent.
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