Searching for synergy in silico, in vitro and in vivo

Antimicrobial Properties of New Polyamines Conjugated with Oxygen-Containing Aromatic Functional Groups

Antibiotic resistance is now a first-order health problem, which makes the development of new families of antimicrobials imperative. These compounds should ideally be inexpensive, readily available, highly active, and non-toxic. Here, we present the results of our investigation regarding the antimicrobial activity of a series of natural and synthetic polyamines with different architectures (linear, tripodal, and macrocyclic) and their derivatives with the oxygen-containing aromatic functional groups 1,3-benzodioxol, ortho/para phenol, or 2,3-dihydrobenzofuran. The new compounds were prepared through an inexpensive process, and their activity was tested against selected strains of yeast, as well as Gram-positive and Gram-negative bacteria. In all cases, the conjugated derivatives showed antimicrobial activity higher than the unsubstituted polyamines. Several factors, such as the overall charge at physiological pH, lipophilicity, and the topology of the polyamine scaffold were relevant to their activity. The nature of the lipophilic moiety was also a determinant of human cell toxicity. The lead compounds were found to be bactericidal and fungistatic, and they were synergic with the commercial antifungals fluconazole, cycloheximide, and amphotericin B against the yeast strains tested.

Synergistic antitumor effect of a penicillin derivative combined with thapsigargin in melanoma cells

PURPOSE

To investigate the effect of TAP7f, a penicillin derivative previously characterized as a potent antitumor agent that promotes ER stress and apoptosis, in combination with thapsigargin, an ER stress inducer, on melanoma cells.

METHODS

The synergistic antiproliferative effect of TAP7f in combination with thapsigargin was studied in vitro in murine B16-F0 melanoma cells, and in human A375 and SB2 melanoma cells. In vivo assays were performed with C57BL/6J mice challenged with B16-F0 cells. Immunofluorescence and Western blot assays were carried out to characterize the induction of ER stress and apoptosis. Necrotic tumor areas and the potential toxicity of the combined therapy were examined by histological analysis of tissue sections after hematoxylin-eosin staining.

RESULTS

In vitro, the combination of TAP7f with thapsigargin synergistically inhibited the proliferation of murine B16-F0, and human A375 and SB2 melanoma cells. When non-inhibitory doses of each drug were simultaneously administered to C57BL/6J mice challenged with B16-F0 cells, a 50% reduction in tumor volumes was obtained in the combined group. An apoptotic response characterized by higher expression levels of Baxenhanced PARP-1 cleavage and the presence of active caspase 3 was observed in tumors from the combined treatment. In addition, higher expression levels of GADD153/CHOP and ATF4 were found in tumors of mice treated with both drugs with respect to each drug used alone, indicating the induction of an ER stress response. No signs of tissue toxicity were observed in histological sections of different organs extracted from mice receiving the combination.

CONCLUSION

The synergistic and effective antitumor action of TAP7f in combination with thapsigargin could be considered as a potential therapeutic strategy for melanoma treatment.

A novel network based linear model for prioritization of synergistic drug combinations

Drug combination therapies can improve drug efficacy, reduce drug dosage, and overcome drug resistance in cancer treatments. Current research strategies to determine which drug combinations have a synergistic effect rely mainly on clinical or empirical experience and screening predefined pools of drugs. Given the number of possible drug combinations, the speed, and scope to find new drug combinations are very limited using these methods. Due to the exponential growth in the number of drug combinations, it is difficult to test all possible combinations in the lab. There are several large-scale public genomic and phenotypic resources that provide data from single drug-treated cells as well as data from small molecule treated cells. These databases provide a wealth of information regarding cellular responses to drugs and offer an opportunity to overcome the limitations of the current methods. Developing a new advanced data processing and analysis strategy is imperative and a computational prediction algorithm is highly desirable. In this paper, we developed a computational algorithm for the enrichment of synergistic drug combinations using gene regulatory network knowledge and an operational module unit (OMU) system which we generate from single drug genomic and phenotypic data. As a proof of principle, we applied the pipeline to a group of anticancer drugs and demonstrate how the algorithm could help researchers efficiently find possible synergistic drug combinations using single drug data to evaluate all possible drug pairs.

Synergistic ideas in oncology: prospects for practical implementation

The review is devoted to the analysis of the problem of synergistic ideas application in oncology after simultaneous combined application of agents. An example of the determination and quantification of the synergistic enhancement ratio is presented. It is emphasized that independent addition is determined by product of probabilities of the effects induced by each agent applied separately. Elevated temperatures synergistically enhance the lethal effect of ionizing radiation and chemical compounds used in the treatment of cancer. Analyzing the dependence of the synergistic effect on the acting temperature after its simultaneous application with ionizing radiation or cisplatin, the existence of an optimal temperature ensuring the greatest synergistic interaction was shown for cultured mammalian and yeast cells. The universal regularities of the manifestation of synergism, independent on the agents, biological objects and tests used, are noted. The greatest synergy is observed with the simultaneous application of agents. The synergism recorded as a result of the combined effects of two factors is observed only with a certain ratio of the effects induced by each agent. Synergism depends on the intensity of the factors used — the current temperature, the dose rate of ionizing radiation or the concentration of chemical agents. These universal patterns have been demonstrated for proand eukaryotic cells, including oncological origin. The existence of universal patterns of synergism indicates the need to develop a new paradigm and theoretical model of synergism, which should take into account the identified patterns. An original biophysical concept of synergistic interaction is proposed. Concrete results are presented that demonstrate the possible ways of using the ideas of synergism in oncology by achieving the greatest synergistic enhancement ratio for the combined effects of various physical and chemical agents. It is concluded that the knowledge and the application of the ideas and general patterns of synergy described in this paper can be useful for specialists using the simultaneous action of various agents to optimize combined treatment methods in modern oncology.

Using Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel Structures

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.

Network Propagation Predicts Drug Synergy in Cancers

Combination therapies are commonly used to treat patients with complex diseases that respond poorly to single-agent therapies. In vitro high-throughput drug screening is a standard method for preclinical prioritization of synergistic drug combinations, but it can be impractical for large drug sets. Computational methods are thus being actively explored; however, most published methods were built on a limited size of cancer cell lines or drugs, and it remains a challenge to predict synergism at a large scale where the diversity within the data escalates the difficulty of prediction. Here, we present a state-of-the-field synergy prediction algorithm, which ranked first in all subchallenges in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. The model was built and evaluated using the largest drug combination screening dataset at the time of the competition, consisting of approximately 11,500 experimentally tested synergy scores of 118 drugs in 85 cancer cell lines. We developed a novel feature extraction strategy by integrating the cross-cell and cross-drug information with a novel network propagation method and then assembled the information in monotherapy and simulated molecular data to predict drug synergy. This represents a significant conceptual advancement of synergy prediction, using extracted features in the form of simulated posttreatment molecular profiles when only the pretreatment molecular profile is available. Our cross-tissue synergism prediction algorithm achieves promising accuracy comparable with the correlation between experimental replicates and can be applied to other cancer cell lines and drugs to guide therapeutic choices.Significance: This study presents a novel network propagation-based method that predicts anticancer drug synergy to the accuracy of experimental replicates, which establishes a state-of-the-field method as benchmarked by the pharmacogenomics research community involving models generated by 160 teams. Cancer Res; 78(18); 5446-57. ©2018 AACR.

In silico prediction of potential chemical reactions mediated by human enzymes

Background

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms.

Result

We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition.

Conclusion

Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

When the Whole is Not Greater than the Sum of the Parts: A Critical Review of Laboratory Bioassay Effects Testing for Insecticidal Protein Interactions

Many studies have been conducted to investigate synergism among insecticidal proteins; however, a consensus on minimal data requirements and interpretation is lacking. While some have concluded that all additive predictive-type models should be abandoned, we advocate that additivity models can remain useful as assessment tools and that an appropriately designed interaction study will never systematically underestimate the existence of synergism, irrespective of which additivity model (or none at all) may be used. To generate the most meaningful synergy assessment datasets in support of safety assessments, we highlight two beneficial steps to follow: (i) select a testing model which is the most consistent with current knowledge regarding the action of the insecticidal proteins and (ii) avoid using bioassay methods which may result in excess response heterogeneity. We also outline other experimental design elements to consider, which may be crucial for conducting future studies of this type. A contrast of underlying testing assumptions associated with the additivity models is provided, along with a comprehensive review of interaction data for Cry1, Cry2, Cry3, Cry9, and Vip3A insecticidal proteins. Our review captures four recurrent findings: i) experiments reporting synergistic interactions are a minority, ii) the degree of synergism reported is low in magnitude, iii) reported interactions are sometimes equivocal/inconclusive due to unconfirmed model assumptions or other bioassay challenges, and iv) due to biological response variation many of the reported interactions may be artefactual. A brief overview of the positioning of interaction testing data in safety assessments of GM food crops is also provided.

Ratiometric drug delivery using non-liposomal nanocarriers as an approach to increase efficacy and safety of combination chemotherapy

The observation that different drug ratios of the same drug combination can lead to synergistic or antagonistic effects when tested against the same cancer cell line in vitro gave rise to a new trend, the ratiometric delivery. This strategy consists of co-encapsulating a specific synergistic ratio of a drug combination into a nanocarrier so that synergism observed in vitro will be faithfully translated to in vivo, optimizing combination therapy. In this review we focus on how to quantify synergism in vitro, followed by how this affected the evolution of nanocarriers culminating in the ratiometric delivery, and finally we summarize the results of the non-liposomal formulations that were built upon this concept.

Biomolecular Network-Based Synergistic Drug Combination Discovery

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

New potential chemotherapy for ovarian cancer - Combined therapy with WP 631 and epothilone B

Despite more modern therapeutics approaches and the use of new drugs for chemotherapy, patients with ovarian cancer still have poor prognosis and therefore, new strategies for its cure are highly needed. One of the promising ways is combined therapy, which has many advantages as minimizing drug resistance, enhancing efficacy of treatment, and reducing toxicity. Combined therapy has rich and successful history in the field of ovarian cancer treatment. Currently use therapy is usually based on platinum-containing agent (carboplatin or cisplatin) and a member of taxanes (paclitaxel or docetaxel). In the mid-2000s this standard regimen has been expanded with bevacizumab, monoclonal antibody directed to Vascular Endothelial Growth Factor (VEGF). Another drug combination with promising perspectives is WP 631 given together with epothilone B (Epo B). WP 631 is a bisanthracycline composed of two molecules of daunorubicin linked with a p-xylenyl linker. Epo B is a 16-membered macrolide manifesting similar mechanism of action to taxanes. Their effectiveness against ovarian cancer as single agents is well established. However, the combination of WP 631 and Epo B appeared to act synergistically, meaning that it is much more potent than the single drugs. The mechanism lying under its efficacy includes disturbing essential cell cycle-regulating proteins leading to mitotic slippage and following apoptosis, as well as affecting EpCAM and HMGB1 expression. In this article, we summarized the current state of knowledge regarding combined therapy based on WP 631 and Epo B as a potential way of ovarian cancer treatment.

Can Invalid Bioactives Undermine Natural Product-Based Drug Discovery?

High-throughput biology has contributed a wealth of data on chemicals, including natural products (NPs). Recently, attention was drawn to certain, predominantly synthetic, compounds that are responsible for disproportionate percentages of hits but are false actives. Spurious bioassay interference led to their designation as pan-assay interference compounds (PAINS). NPs lack comparable scrutiny, which this study aims to rectify. Systematic mining of 80+ years of the phytochemistry and biology literature, using the NAPRALERT database, revealed that only 39 compounds represent the NPs most reported by occurrence, activity, and distinct activity. Over 50% are not explained by phenomena known for synthetic libraries, and all had manifold ascribed bioactivities, designating them as invalid metabolic panaceas (IMPs). Cumulative distributions of ∼200,000 NPs uncovered that NP research follows power-law characteristics typical for behavioral phenomena. Projection into occurrence–bioactivity–effort space produces the hyperbolic black hole of NPs, where IMPs populate the high-effort base.

Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

What is synergy? The Saariselkä agreement revisited

Many biological or chemical agents when combined interact with each other and produce a synergistic response that cannot be predicted based on the single agent responses alone. However, depending on the postulated null hypothesis of non-interaction, one may end up in different interpretations of synergy. Two popular reference models for null hypothesis include the Bliss independence model and the Loewe additivity model, each of which is formulated from different perspectives. During the last century, there has been an intensive debate on the suitability of these synergy models, both of which are theoretically justified and also in practice supported by different schools of scientists. More than 20 years ago, there was a community effort to make a consensus on the terminology one should use when claiming synergy. The agreement was formulated at a conference held in Saariselkä, Finland in 1992, stating that one should use the terms Bliss synergy or Loewe synergy to avoid ambiguity in the underlying models. We review the theoretical relationships between these models and argue that one should combine the advantages of both models to provide a more consistent definition of synergy and antagonism.