Understanding synergy

Current Methods for Quantifying Drug Synergism

The effectiveness of drug combinations for treatment of a variety of complex diseases is well established. "Drug cocktail" treatments are often prescribed to improve the overall efficacy, decrease toxicity, alter pharmacodynamics, etc in an overall treatment strategy. Specifically, if when combined, drugs interact in some way that causes the total effect to be greater than that predicted by their individual potencies, then drugs are considered synergistic. While there are established ways to quantify the impact of drug combinations clinically, it is an open challenge to quantitatively summarize a synergistic interaction. In this paper, we discuss an overview of the current statistical and mathematical methods for the study of drug combination effects, especially drug synergy quantification (where the interaction effect is not just detected, but quantified according to its magnitude). We first introduce two popular reference models for testing to null hypothesis of non-interaction for a combination, including the Bliss independence model and the Loewe additivity model. Then we discuss several methods for quantifying drug synergism. The advantages and disadvantages with these methods are also provided, and finally, we discuss important next directions in this area.

In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data

Chemotherapy is a routine treatment approach for early-stage cancers, but the effectiveness of such treatments is often limited by drug resistance, toxicity, and tumor heterogeneity. Combination chemotherapy, in which two or more drugs are applied simultaneously, offers one promising approach to address these concerns, since two single-target drugs may synergize with one another through interconnected biological processes. However, the identification of effective dual therapies has been particularly challenging; because the search space is large, combination success rates are low. Here, we present our method for DREAM AstraZeneca-Sanger Drug Combination Prediction Challenge to predict synergistic drug combinations. Our approach involves using biologically relevant drug and cell line features with machine learning. Our machine learning model obtained the primary metric = 0.36 and the tie-breaker metric = 0.37 in the extension round of the challenge which was ranked in top 15 out of 76 submissions. Our approach also achieves a mean primary metric of 0.39 with ten repetitions of 10-fold cross-validation. Further, we analyzed our model's predictions to better understand the molecular processes underlying synergy and discovered that key regulators of tumorigenesis such as TNFA and BRAF are often targets in synergistic interactions, while MYC is often duplicated. Through further analysis of our predictions, we were also ble to gain insight into mechanisms and potential biomarkers of synergistic drug pairs.

Synergy and antagonism in natural product extracts: when 1 + 1 does not equal 2

Covering: 2000 to 2019 According to a 2012 survey from the Centers for Disease Control and Prevention, approximately 18% of the U.S. population uses natural products (including plant-based or botanical preparations) for treatment or prevention of disease. The use of plant-based medicines is even more prevalent in developing countries, where for many they constitute the primary health care modality. Proponents of the medicinal use of natural product mixtures often claim that they are more effective than purified compounds due to beneficial "synergistic" interactions. A less-discussed phenomenon, antagonism, in which effects of active constituents are masked by other compounds in a complex mixture, also occurs in natural product mixtures. Synergy and antagonism are notoriously difficult to study in a rigorous fashion, particularly given that natural products chemistry research methodology is typically devoted to reducing complexity and identifying single active constituents for drug development. This report represents a critical review with commentary about the current state of the scientific literature as it relates to studying combination effects (including both synergy and antagonism) in natural product extracts. We provide particular emphasis on analytical and Big Data approaches for identifying synergistic or antagonistic combinations and elucidating the mechanisms that underlie their interactions. Specific case studies of botanicals in which synergistic interactions have been documented are also discussed. The topic of synergy is important given that consumer use of botanical natural products and associated safety concerns continue to garner attention by the public and the media. Guidance by the natural products community is needed to provide strategies for effective evaluation of safety and toxicity of botanical mixtures and to drive discovery in botanical natural product research.

Synergy theory for murine Harderian gland tumours after irradiation by mixtures of high-energy ionized atomic nuclei

Experimental studies reporting murine Harderian gland (HG) tumourigenesis have been a NASA concern for many years. Studies used particle accelerators to produce beams that, on beam entry, consist of a single isotope also present in the galactic cosmic ray (GCR) spectrum. In this paper synergy theory is described, potentially applicable to corresponding mixed-field experiments, in progress, planned, or hypothetical. The "obvious" simple effect additivity (SEA) approach of comparing an observed mixture dose-effect relationship (DER) to the sum of the components' DERs is known from other fields of biology to be unreliable when the components' DERs are highly curvilinear. Such curvilinearity may be present at low fluxes such as those used in the one-ion HG experiments due to non-targeted ('bystander') effects, in which case a replacement for SEA synergy theory is needed. This paper comprises in silico modeling of published experimental data using a recently introduced, arguably optimal, replacement for SEA: incremental effect additivity (IEA). Customized open-source software is used. IEA is based on computer numerical integration of non-linear ordinary differential equations. To illustrate IEA synergy theory, possible rapidly-sequential-beam mixture experiments are discussed, including tight 95% confidence intervals calculated by Monte-Carlo sampling from variance-covariance matrices. The importance of having matched one-ion and mixed-beam experiments is emphasized. Arguments are presented against NASA over-emphasizing accelerator experiments with mixed beams whose dosing protocols are standardized rather than being adjustable to take biological variability into account. It is currently unknown whether mixed GCR beams sometimes have statistically significant synergy for the carcinogenesis endpoint. Synergy would increase risks for prolonged astronaut voyages in interplanetary space.

Comparison of null models for combination drug therapy reveals Hand model as biochemically most plausible

Null models for the effect of combination therapies are widely used to evaluate synergy and antagonism of drugs. Due to the relevance of null models, their suitability is continuously discussed. Here, we contribute to the discussion by investigating the properties of five null models. Our study includes the model proposed by David J. Hand, which we refer to as Hand model. The Hand model has been introduced almost 20 years ago but hardly was used and studied. We show that the Hand model generalizes the principle of dose equivalence compared to the Loewe model and resolves the ambiguity of the Tallarida model. This provides a solution to the persisting conflict about the compatibility of two essential model properties: the sham combination principle and the principle of dose equivalence. By embedding several null models into a common framework, we shed light in their biochemical validity and provide indications that the Hand model is biochemically most plausible. We illustrate the practical implications and differences between null models by examining differences of null models on published data.

Prediction of drug synergy score using ensemble based differential evolution

Prediction of drug synergy score is an ill-posed problem. It plays an efficient role in the medical field for inhibiting specific cancer agents. An efficient regression-based machine learning technique has an ability to minimise the drug synergy prediction errors. Therefore, in this study, an efficient machine learning technique for drug synergy prediction technique is designed by using ensemble based differential evolution (DE) for optimising the support vector machine (SVM). Because the tuning of the attributes of SVM kernel regulates the prediction precision. The ensemble based DE employs two trial vector generation techniques and two control attributes settings. The initial generation technique has the best solution and the other is without the best solution. The proposed and existing competitive machine learning techniques are applied to drug synergy data. The extensive analysis demonstrates that the proposed technique outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Pairwise combinations of chemical compounds that delay yeast chronological aging through different signaling pathways display synergistic effects on the extent of aging delay

We have recently discovered six plant extracts that delay yeast chronological aging. Most of them affect different nodes, edges and modules of an evolutionarily conserved network of longevity regulation that integrates certain signaling pathways and protein kinases; this network is also under control of such aging-delaying chemical compounds as spermidine and resveratrol. We have previously shown that, if a strain carrying an aging-delaying single-gene mutation affecting a certain node, edge or module of the network is exposed to some of the six plant extracts, the mutation and the plant extract enhance aging-delaying efficiencies of each other so that their combination has a synergistic effect on the extent of aging delay. We therefore hypothesized that a pairwise combination of two aging-delaying plant extracts or a combination of one of these plant extracts and spermidine or resveratrol may have a synergistic effect on the extent of aging delay only if each component of this combination targets a different element of the network. To test our hypothesis, we assessed longevity-extending efficiencies of all possible pairwise combinations of the six plant extracts or of one of them and spermidine or resveratrol in chronologically aging yeast. In support of our hypothesis, we show that only pairwise combinations of naturally-occurring chemical compounds that slow aging through different nodes, edges and modules of the network delay aging in a synergistic manner.

Rhinovirus and Bacteria Synergistically Induce IL-17C Release from Human Airway Epithelial Cells To Promote Neutrophil Recruitment

Virus-bacteria coinfections are associated with more severe exacerbations and increased risk of hospital readmission in patients with chronic obstructive pulmonary disease (COPD). The airway epithelium responds to such infections by releasing proinflammatory and antimicrobial cytokines, including IL-17C. However, the regulation and role of IL-17C is not well understood. In this study, we examine the mechanisms regulating IL-17C production and its potential role in COPD exacerbations. Human bronchial epithelial cells (HBE) obtained from normal, nontransplanted lungs or from brushings of nonsmokers, healthy smokers, or COPD patients were exposed to bacteria and/or human rhinovirus (HRV). RNA and protein were collected for analysis, and signaling pathways were assessed with pharmacological agonists, inhibitors, or small interfering RNAs. HBE were also stimulated with IL-17C to assess function. HRV-bacterial coinfections synergistically induced IL-17C expression. This induction was dependent on HRV replication and required NF-κB-mediated signaling. Synergy was lost in the presence of an inhibitor of the p38 MAP kinase pathway. HBE exposed to IL-17C show increased gene expression of CXCL1, CXCL2, NFKBIZ, and TFRC, and release CXCL1 protein, a neutrophil chemoattractant. Knockdown of IL-17C significantly reduced induction of CXCL1 in response to HRV-bacterial coinfection as well as neutrophil chemotaxis. HBE from healthy smokers release less IL-17C than cells from nonsmokers, but cells from COPD patients release significantly more IL-17C compared with either nonsmokers or healthy smokers. These data suggest that IL-17C may contribute to microbial-induced COPD exacerbations by promoting neutrophil recruitment.

Analysis of chemical composition and in vitro antidermatophyte activity of ethanol extracts of Dryopteris fragrans (L.) Schott

ETHNOPHARMACOLOGICAL RELEVANCE

Dryopteris fragrans (L.) Schott is a deciduous perennial herb, which has been used traditionally for treatment of ringworm infections and others skin diseases in the north of China.

AIM OF THE STUDY

To characterize the chemical composition, evaluate the antifungal activity and explore possible mechanisms about action of ethanol extracts of D. fragrans.

MATERIALS AND METHODS

The chemical components in the ethanol extracts of D. fragrans were detected by high-performance liquid chromatography coupled with electrospray ionization and quadruple time-of-flight mass spectrometry (HPLC-ESI-QTOF-MS/MS). The minimal inhibitory concentrations (MIC) and minimal fungicidal concentrations (MFC) of the ethanol extracts of D. fragrans were determined by the Clinical and Laboratory Standards Institute (CLSI) M38-A2 method against 62 isolates of dermatophytes. The kinetics of fungal kill, synergy testing by checkerboard dilution and quantitation of sterol by ultra-performance liquid chromatography (UPLC) on Trichophyton rubrum and Trichophyton mentagrophytes were also investigated.

RESULTS

Fourteen derivatives of phloroglucinol were identified in the ethanol extracts of D. fragrans. The MIC of the ethanol extracts of D. fragrans ranged from 0.059 to 3.780 mg/mL while MFC ranged from 0.118 to 3.780 mg/mL. The ethanol extracts of D. fragrans exerted fungicidal activity after 12 h of incubation against Trichophyton rubrum while it required 36 h of incubation against Trichophyton mentagrophytes at concentrations of 8 × MIC. In synergy testing, the interaction between miconazole (MCZ) and terbinafine (TBF) with the ethanol extracts of D. fragrans proved to be indifferent by testing fractional inhibitory concentration (FIC) values. Sterol in samples of fungal cells treated with the ethanol extracts of D. fragrans was significantly reduced.

CONCLUSIONS

The ethanol extracts of D. fragrans had antifungal and fungicidal activity against dermatophytes and was likely a strain-dependent fungicidal agent. Interaction between drugs was indifferent on tested isolates. The inhibition of ergosterol biosynthesis was one of the antifungal mechanisms of the ethanol extracts of D. fragrans. These results showed that the ethanol extracts of D. fragrans could be explored for promising antifungal drugs. Dozens of phloroglucinol derivatives may contribute to high antifungal activity of the ethanol extracts of D. fragrans.

Functional Assessment of 2,177 U.S. and International Drugs Identifies the Quinoline Nitroxoline as a Potent Amoebicidal Agent against the Pathogen Balamuthia mandrillaris

Balamuthia mandrillaris is responsible for hundreds of reported cases of amoebic encephalitis, the majority of which have been fatal. Despite being an exceptionally deadly pathogen, B. mandrillaris is understudied, leaving many open questions regarding epidemiology, diagnosis, and treatment. Due to the lack of effective drugs to fight B. mandrillaris infections, mortality rates remain high even for patients receiving intensive care. This report addresses the need for new treatment options through a drug repurposing screen to identify novel B. mandrillaris inhibitors. The most promising candidate identified was the quinoline antibiotic nitroxoline, which has a long history of safe use in humans. We show that nitroxoline kills B. mandrillaris at pharmacologically relevant concentrations and exhibits greater potency and selectivity than drugs commonly used in the current standard of care. The findings that we present demonstrate the potential of nitroxoline to be an important new tool in the treatment of life-threatening B. mandrillaris infections.

Balamuthia mandrillaris is a pathogenic free-living amoeba that causes a rare but almost always fatal infection of the central nervous system called granulomatous amoebic encephalitis (GAE). Two distinct forms of B. mandrillaris—a proliferative trophozoite form and a nonproliferative cyst form, which is highly resistant to harsh physical and chemical conditions—have been isolated from environmental samples worldwide and are both observed in infected tissue. Patients suffering from GAE are typically treated with aggressive and prolonged multidrug regimens that often include the antimicrobial agents miltefosine and pentamidine isethionate. However, survival rates remain low, and studies evaluating the susceptibility of B. mandrillaris to these compounds and other potential therapeutics are limited. To address the need for more-effective treatments, we screened 2,177 clinically approved compounds for in vitro activity against B. mandrillaris. The quinoline antibiotic nitroxoline (8-hydroxy-5-nitroquinoline), which has safely been used in humans to treat urinary tract infections, was identified as a lead compound. We show that nitroxoline inhibits both trophozoites and cysts at low micromolar concentrations, which are within a pharmacologically relevant range. We compared the in vitro efficacy of nitroxoline to that of drugs currently used in the standard of care for GAE and found that nitroxoline is the most potent and selective inhibitor of B. mandrillaris tested. Furthermore, we demonstrate that nitroxoline prevents B. mandrillaris-mediated destruction of host cells in cultured fibroblast and primary brain explant models also at pharmacologically relevant concentrations. Taken together, our findings indicate that nitroxoline is a promising candidate for repurposing as a novel treatment of B. mandrillaris infections.

Simultaneous Inhibition of MEK and Hh Signaling Reduces Pancreatic Cancer Metastasis

Pancreatic cancer, mostly pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancer types, with an estimated 44,330 death in 2018 in the US alone. While targeted therapies and immune checkpoint inhibitors have significantly improved treatment options for patients with lung cancer and renal cell carcinomas, little progress has been made in pancreatic cancer, with a dismal 5-year survival rate currently at ~8%. Upon diagnosis, the majority of pancreatic cancer cases (~80%) are already metastatic. Thus, identifying ways to reduce pancreatic cancer metastasis is an unmet medical need. Furthermore, pancreatic cancer is notorious resistant to chemotherapy. While Kirsten RAt Sarcoma virus oncogene (K-RAS) mutation is the major driver for pancreatic cancer, specific inhibition of RAS signaling has been very challenging, and combination therapy is thought to be promising. In this study, we report that combination of hedgehog (Hh) and Mitogen-activated Protein/Extracellular Signal-regulated Kinase Kinase (MEK) signaling inhibitors reduces pancreatic cancer metastasis in mouse models. In mouse models of pancreatic cancer metastasis using human pancreatic cancer cells, we found that Hh target gene Gli1 is up-regulated during pancreatic cancer metastasis. Specific inhibition of smoothened signaling significantly altered the gene expression profile of the tumor microenvironment but had no significant effects on cancer metastasis. By combining Hh signaling inhibitor BMS833923 with RAS downstream MEK signaling inhibitor AZD6244, we observed reduced number of metastatic nodules in several mouse models for pancreatic cancer metastasis. These two inhibitors also decreased cell proliferation significantly and reduced CD45⁺ cells (particularly Ly6G⁺CD11b⁺ cells). We demonstrated that depleting Ly6G⁺ CD11b⁺ cells is sufficient to reduce cancer cell proliferation and the number of metastatic nodules. In vitro, Ly6G⁺ CD11b⁺ cells can stimulate cancer cell proliferation, and this effect is sensitive to MEK and Hh inhibition. Our studies may help design novel therapeutic strategies to mitigate pancreatic cancer metastasis.

Applying the Monod-Wyman-Changeux Allosteric Activation Model to Pseudo-Steady-State Responses from GABAA Receptors

The Monod-Wyman-Changeux (MWC) cyclic model was described as a kinetic scheme to explain enzyme function and modulation more than 50 years ago and was proposed as a model for understanding the activation of transmitter-gated channels soon afterward. More recently, the MWC model has been used to describe the activation of the GABA_A receptor by the transmitter, GABA, and drugs that bind to separate sites on the receptor. It is most interesting that the MWC formalism can also describe the interactions among drugs that activate the receptor. In this review, we describe properties of the MWC model that have been explored experimentally using the GABA_A receptor, summarize analytical expressions for activation and interaction for drugs, and briefly review experimental results.

The Synergistic Combination of Everolimus and Paroxetine Exerts Post-ischemic Neuroprotection In Vitro

Ischemic stroke is a debilitating multi-factorial cerebrovascular disorder, representing an area of tremendous unmet medical need. Combination treatment has been proposed as a promising therapeutic approach towards combating ischemic stroke. The present study employs in vitro oxygen glucose deprivation (OGD) model to evaluate the post-ischemic neuroprotective efficacy of Everolimus and Paroxetine, alone and in combination. Post-OGD treatment with Everolimus and Paroxetine, alone or in combination, significantly improved the cell survival (~ 80%) when compared to the cells subjected to ischemic injury alone. The individual neuroprotective doses of Everolimus and Paroxetine were found to be at 6.25 and 25 nM, respectively. Whereas, the synergistic neuroprotective dose for Everolimus:Paroxetine was 2:10 nM, calculated using the Chou-Talalay combination index and other four mathematical models. The synergistic combination dose downregulated neuroinflammatory genes (Tnf-α, Il1b, Nf-κB, and iNos) and upregulated the neuroprotective genes (Bcl-2, Bcl-xl, Hif-1, and Epo). The mitochondrial functioning and ROS neutralizing ability increased with combination treatment. Further, the active role of nitric oxide synthase and calmodulin were revealed while exploring the bio-activity of Everolimus and Paroxetine through network pharmacology. The present study for the first time demonstrates the synergistic post-ischemic neuroprotective efficacy of combination treatment with Everolimus and Paroxetine in vitro. Taken together, these findings clearly suggest that Everolimus in combination with Paroxetine may represent a promising therapeutic strategy for the treatment of ischemic stroke, further supporting the combination treatment strategy for this debilitating disorder.

A combination of curcumin and oligomeric proanthocyanidins offer superior anti-tumorigenic properties in colorectal cancer

Combining anti-cancer agents in cancer therapies is becoming increasingly popular due to improved efficacy, reduced toxicity and decreased emergence of resistance. Here, we test the hypothesis that dietary agents such as oligomeric proanthocyanidins (OPCs) and curcumin cooperatively modulate cancer-associated cellular mechanisms to inhibit carcinogenesis. By a series of in vitro assays in colorectal cancer cell lines, we showed that the anti-tumorigenic properties of the OPCs-curcumin combination were superior to the effects of individual compounds. By RNA-sequencing based gene-expression profiling in six colorectal cancer cell lines, we identified the cooperative modulation of key cancer-associated pathways such as DNA replication and cell cycle pathways. Moreover, several pathways, including protein export, glutathione metabolism and porphyrin metabolism were more effectively modulated by the combination of OPCs and curcumin. We validated genes belonging to these pathways, such as HSPA5, SEC61B, G6PD, HMOX1 and PDE3B to be cooperatively modulated by the OPCs-curcumin combination. We further confirmed that the OPCs-curcumin combination more potently suppresses colorectal carcinogenesis and modulated expression of genes identified by RNA-sequencing in mice xenografts and in colorectal cancer patient-derived organoids. Overall, by delineating the cooperative mechanisms of action of OPCs and curcumin, we make a case for the clinical co-administration of curcumin and OPCs as a treatment therapy for patients with colorectal cancer.

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.

Harnessing synthetic lethality to predict the response to cancer treatment

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

An integrated framework for identification of effective and synergistic anti-cancer drug combinations

Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism. There is a need to model the combination drug screening data to predict new synergistic drug combinations for successful cancer treatment. In such a scenario, machine learning approaches can be used to alleviate the process of drugs synergy prediction. Experimental data from a single-agent or multi-agent drug screens provides feature data for model training. On the contrary, identification of effective drug combination using clinical trials is a time consuming and resource intensive task. This paper attempts to address the aforementioned challenges by developing a computational approach to effectively predict drug synergy. Single-drug efficacy is used for predicting drug synergism. Our approach obviates the need to understand the underlying drug mechanism to predict drug combination synergy. For this purpose, nine machine learning algorithms are trained. It is observed that the Random forest models, in comparison to other models, have shown significant performance. The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> -fold cross-validation is performed to evaluate the robustness of the best predictive model. The proposed approach is applied to mutant-BRAF melanoma and further validated using melanoma cell-lines from AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge dataset.

Feasibility of Exposure-Response Analyses for Clinical Dose-Ranging Studies of Drug Combinations

The exposure-response relationship of combinatory drug effects can be quantitatively described using pharmacodynamic interaction models, which can be used for the selection of optimal dose combinations. The aim of this simulation study was to evaluate the reliability of parameter estimates and the probability for accurate dose identification for various underlying exposure-response profiles, under a number of different phase II designs. An efficacy variable driven by the combined exposure of two theoretical compounds was simulated and model parameters were estimated using two different models, one estimating all parameters and one assuming that adequate previous knowledge for one drug is readily available. Estimation of all pharmacodynamic parameters under a realistic, in terms of sample size and study design, phase II trial, proved to be challenging. Inaccurate estimates were found in all exposure-response scenarios, except for situations where no pharmacodynamic interaction was present, with the drug potency and interaction parameters being the hardest to estimate. When previous knowledge of the exposure-response relationship of one of the monocomponents is available, such information should be utilized, as it enabled relevant improvements in parameter estimation and in correct dose identification. No general trends for classification of the performance of the tested study designs across different scenarios could be identified. This study shows that pharmacodynamic interactions models can be used for the exposure-response analysis of clinical endpoints especially when accompanied by appropriate dose selection in regard to the expected drug potencies and appropriate trial size and if information regarding the exposure-response profile of one monocomponent is available.

Combinatorial therapeutic targeting of BMP2 and MEK-ERK pathways in NF1-associated malignant peripheral nerve sheath tumors

The clinical management of malignant peripheral nerve sheath tumors (MPNSTs) is challenging not only due to its aggressive and invasive nature, but also limited therapeutic options. Using gene expression profiling, our lab identified BMP2-SMAD1/5/8 pathway as a potential therapeutic target for treating MPNSTs. In this study, we explored the therapeutic impact of targeting BMP2-SMAD1/5/8 pathway in conjunction with RAS-MEK-ERK signaling, which is constitutively activated in MPNSTs. Our results indicated that single agent treatment with LDN-193189, a BMP2 Type I receptor inhibitor, did not affect the growth and survival of MPNST cells at biochemically relevant inhibitory concentrations. However, addition of a MEK1/2 inhibitor, selumetinib, to LDN-193189-treated cells resulted in significant inhibition of cell growth and induction of cell death. LDN-193189 at biochemically effective concentrations significantly inhibited motility and invasiveness of MPNST cells, and these effects were enhanced by the addition of selumetinib. Overall, our results advocate for a combinatorial therapeutic approach for MPNSTs that not only targets the growth and survival via inhibition of MEK1/2, but also its malignant spread by suppressing the activation of BMP2-SMAD1/5/8 pathway. Importantly, these studies were conducted in low-passage patient-derived MPNST cells, allowing for an investigation of the effects of the proposed drug treatments in a biologically-relevant context.

Additive Dose Response Models: Explicit Formulation and the Loewe Additivity Consistency Condition

High-throughput techniques allow for massive screening of drug combinations. To find combinations that exhibit an interaction effect, one filters for promising compound combinations by comparing to a response without interaction. A common principle for no interaction is Loewe Additivity which is based on the assumption that no compound interacts with itself and that two doses from different compounds having the same effect are equivalent. It then should not matter whether a component is replaced by the other or vice versa. We call this assumption the Loewe Additivity Consistency Condition (LACC). We derive explicit and implicit null reference models from the Loewe Additivity principle that are equivalent when the LACC holds. Of these two formulations, the implicit formulation is the known General Isobole Equation (Loewe, 1928), whereas the explicit one is the novel contribution. The LACC is violated in a significant number of cases. In this scenario the models make different predictions. We analyze two data sets of drug screening that are non-interactive (Cokol et al., 2011; Yadav et al., 2015) and show that the LACC is mostly violated and Loewe Additivity not defined. Further, we compare the measurements of the non-interactive cases of both data sets to the theoretical null reference models in terms of bias and mean squared error. We demonstrate that the explicit formulation of the null reference model leads to smaller mean squared errors than the implicit one and is much faster to compute.

A SYSTEMATIC APPROACH FOR ASSESSING, IN THE ABSENCE OF FULL EVIDENCE, WHETHER MULTICOMPONENT INTERVENTIONS CAN BE MORE COST-EFFECTIVE THAN SINGLE COMPONENT INTERVENTIONS

OBJECTIVES

Multicomponent interventions (MCIs), consisting of at least two interventions, are common in rehabilitation and other healthcare fields. When the effectiveness of the MCI versus that of its single interventions is comparable or unknown, evidence of their expected incremental cost-effectiveness can be helpful in deciding which intervention to recommend. As such evidence often is unavailable this study proposes an approach to estimate what is more cost-effective; the MCI or the single intervention(s).

METHODS

We reviewed the literature for potential methods. Of those identified, headroom analysis was selected as the most suitable basis for developing the approach, based on the criteria of being able to estimate the cost-effectiveness of the single interventions versus that of the MCI (a) within a limited time frame, (b) in the absence of full data, and (c) taking into account carry-over and interaction effects. We illustrated the approach with an MCI for cancer survivors.

RESULTS

The approach starts with analyzing the costs of the MCI. Given a specific willingness-to-pay-value, it is analyzed how much effectiveness the MCI would need to generate to be considered cost-effective, and if this is likely to be attained. Finally, the cost-effectiveness of the single interventions relative to the potential of the MCI for being cost-effective can be compared.

CONCLUSIONS

A systematic approach using headroom analysis was developed for estimating whether an MCI is likely to be more cost effective than one (or more) of its single interventions.

Suppressive drug combinations and their potential to combat antibiotic resistance

Antibiotic effectiveness often changes when two or more such drugs are administered simultaneously and unearthing antibiotic combinations with enhanced efficacy (synergy) has been a longstanding clinical goal. However, antibiotic resistance, which undermines individual drugs, threatens such combined treatments. Remarkably, it has emerged that antibiotic combinations whose combined effect is lower than that of at least one of the individual drugs can slow or even reverse the evolution of resistance. We synthesize and review studies of such so-called 'suppressive interactions' in the literature. We examine why these interactions have been largely disregarded in the past, the strategies used to identify them, their mechanistic basis, demonstrations of their potential to reverse the evolution of resistance and arguments for and against using them in clinical treatment. We suggest future directions for research on these interactions, aiming to expand the basic body of knowledge on suppression and to determine the applicability of suppressive interactions in the clinic.

The Actions of Drug Combinations on the GABAA Receptor Manifest as Curvilinear Isoboles of Additivity

Drug interactions are often analyzed in terms of isobolograms. In the isobologram, the line connecting the axial points corresponding to the concentrations of two different drugs that produce an effect of the same magnitude is termed an isobole of additivity. Although the isobole of additivity can be a straight line in some special cases, previous work has proposed that it is curvilinear when the two drugs differ in their maximal effects or Hill slopes. Modulators of transmitter-gated ion channels have a wide range of maximal effects as well as Hill slopes, suggesting that the isoboles for drug actions on ion channel function are not linear. In this study, we have conducted an analysis of direct activation and potentiation of the human α1β2γ2L GABA_A receptor to demonstrate that: 1) curvilinear isoboles of additivity are predicted by a concerted transition model where the binding of each GABAergic drug additively and independently reduces the free energy of the open receptor compared with the closed receptor; and 2) experimental data for receptor activation using the agonist pair of GABA and propofol or potentiation of responses to a low concentration of GABA by the drug pair of alfaxalone and propofol agree very well with predictions. The approach assuming independent energetic contributions from GABAergic drugs enables, at least for the drug combinations tested, a straightforward method to accurately predict functional responses to any combination of concentrations.

Theory of synergistic effects: Hill-type response surfaces as 'null-interaction' models for mixtures

BACKGROUND

The classification of effects caused by mixtures of agents as synergistic, antagonistic or additive depends critically on the reference model of 'null interaction'. Two main approaches are currently in use, the Additive Dose (ADM) or concentration addition (CA) and the Multiplicative Survival (MSM) or independent action (IA) models. We compare several response surface models to a newly developed Hill response surface, obtained by solving a logistic partial differential equation (PDE). Assuming that a mixture of chemicals with individual Hill-type dose-response curves can be described by an n-dimensional logistic function, Hill's differential equation for pure agents is replaced by a PDE for mixtures whose solution provides Hill surfaces as 'null-interaction' models and relies neither on Bliss independence or Loewe additivity nor uses Chou's unified general theory.

METHODS

An n-dimensional logistic PDE decribing the Hill-type response of n-component mixtures is solved. Appropriate boundary conditions ensure the correct asymptotic behaviour. Mathematica 11 (Wolfram, Mathematica Version 11.0, 2016) is used for the mathematics and graphics presented in this article.

RESULTS

The Hill response surface ansatz can be applied to mixtures of compounds with arbitrary Hill parameters. Restrictions which are required when deriving analytical expressions for response surfaces from other principles, are unnecessary. Many approaches based on Loewe additivity turn out be special cases of the Hill approach whose increased flexibility permits a better description of 'null-effect' responses. Missing sham-compliance of Bliss IA, known as Colby's model in agrochemistry, leads to incompatibility with the Hill surface ansatz. Examples of binary and ternary mixtures illustrate the differences between the approaches. For Hill-slopes close to one and doses below the half-maximum effect doses MSM (Colby, Bliss, Finney, Abbott) predicts synergistic effects where the Hill model indicates 'null-interaction'. These differences increase considerably with increasing steepness of the individual dose-response curves.

CONCLUSION

The Hill response surface ansatz contains the Loewe additivity concept as a special case and is incompatible with Bliss independent action. Hence, when synergistic effects are claimed, those dose combinations deserve special attention where the differences between independent action approaches and Hill estimations are large.

Scientific familial lessons in ingestive behavior research: 2016 Alan N. Epstein research award

While energy balance is under the control of the central nervous system (CNS), a major source of neural regulation for the behavioral, physiological and endocrine processes governing energy balance originates in the periphery. Indeed, the organs of the gastrointestinal (GI) tract, supporting organs of the peritoneal cavity and adipose tissue are the source of numerous neurotransmitter and neuroendocrine signals released from non-neuronal peripheral tissue that signal in a paracrine and endocrine fashion to regulate the physiological and behavioral processes that affect energy balance. Given the ever increasing appreciation that chronic hyperphagia of highly-palatable/rewarding food is a major contributing factor to the obesity epidemic, it is not surprising that the field has increased research efforts focusing on understanding what role peripherally-derived neuroendocrine signals play in modulating food reward and motivated behaviors. Research throughout my career has focused on understanding gut-to-brain communication of relevance to energy balance control. Through very fortuitous opportunities and amazing collaborations, my research program has also expanded widely to include analyses of multiple GI-, pancreatic- and adipose tissue-derived anorectic signals involved in food intake and energy balance control, as well as analyses of higher-order determinants of food reward, nausea, aversion and maladaptive motivated behaviors. I am honored to be the recipient of the 2016 Alan N. Epstein Research Award from the Society for the Study of Ingestive Behavior, and express much appreciation for the amazing collaborations I have had with my mentors, colleagues and trainees.

Combination therapy for the treatment of pulmonary mold infections

INTRODUCTION

Pulmonary mold infections are caused by ubiquitous organisms found in soil, water, and decaying vegetation, including Aspergillus spp., the Mucormycetes, hyaline molds, and dematiaceous (black) molds. Areas covered: These infections are often a challenge to diagnose and even more difficult to treat. Recently, antifungal combination therapy has emerged as a promising strategy to treat some forms of invasive mycoses, including pulmonary mold infections. Historically, this approach has been limited due to non-uniform interpretation criteria, variations in pharmacodynamic/pharmacokinetic properties of antifungals used in combination, and an inability to predict clinical success based on in vitro data and animal models. However, recent advances have helped mitigate some of these challenges. Expert commentary: In this paper, we explore what is known about the antifungal combination therapy in the treatment of pulmonary mold infections and explore how it may impact clinical practice. We pay particular attention to novel combinations and the challenges associated with the development of new antifungal agents.

Identification of deubiquitinase targets of isothiocyanates using SILAC-assisted quantitative mass spectrometry

Cruciferous vegetables such as broccoli and kale have well documented chemopreventative and anticancer effects that are attributed to the presence of isothiocyanates (ITCs). ITCs modulate the levels of many oncogenic proteins, but the molecular mechanisms of ITC action are not understood. We previously reported that phenethyl isothiocyanate (PEITC) inhibits two deubiquitinases (DUBs), USP9x and UCH37. DUBs regulate many cellular processes and DUB dysregulation is linked to the pathogenesis of human diseases including cancer, neurodegeneration, and inflammation. Using SILAC assisted quantitative mass spectrometry, here we identify 9 new PEITC-DUB targets: USP1, USP3, USP10, USP11, USP16, USP22, USP40, USP48 and VCPIP1. Seven of these PEITC-sensitive DUBs have well-recognized roles in DNA repair or chromatin remodeling. PEITC both inhibits USP1 and increases its ubiquitination and degradation, thus decreasing USP1 activity by two mechanisms. The loss of USP1 activity increases the level of mono-ubiquitinated DNA clamp PCNA, impairing DNA repair. Both the inhibition/degradation of USP1 and the increase in mono-ubiquitinated PCNA are new activities for PEITC that can explain the previously recognized ability of ITCs to enhance cancer cell sensitivity to cisplatin treatment. Our work also demonstrates that PEITC reduces the mono-ubiquityl histones H2A and H2B. Understanding the mechanism of action of ITCs should facilitate their use as therapeutic agents.

An Introduction to Terminology and Methodology of Chemical Synergy-Perspectives from Across Disciplines

The idea of synergistic interactions between drugs and chemicals has been an important issue in the biomedical world for over a century. As complex diseases, especially cancer, are being treated with various drug cocktails, understanding the interactions among these drugs is increasingly vital to ensuring successful treatment regimens. However, the idea of synergy is not limited to only the biomedical realm and these ideas have developed across many different disciplines, as well. In this review, we first discuss the various terminology surrounding the idea of synergy, providing a comprehensive list of terms defined across numerous disciplines. We then review the most common methodology for detection and quantification of synergy, including the two most prominent reference models for describing additive interactions: Loewe Additivity and Bliss Independence. We also discuss advantages and limitations to each method, with a focus on the Chou-Talalay Combination Index method. Finally, we describe how methods development and terminology have developed among disciplines outside of biomedicine and pharmacology, to synthesize the literature for readers.

Tumor-treating fields elicit a conditional vulnerability to ionizing radiation via the downregulation of BRCA1 signaling and reduced DNA double-strand break repair capacity in non-small cell lung cancer cell lines

The use of tumor-treating fields (TTFields) has revolutionized the treatment of recurrent and newly diagnosed glioblastoma (GBM). TTFields are low-intensity, intermediate frequency, alternating electric fields that are applied to tumor regions and cells using non-invasive arrays. The predominant mechanism by which TTFields are thought to kill tumor cells is the disruption of mitosis. Using five non-small cell lung cancer (NSCLC) cell lines we found that there is a variable response in cell proliferation and cell killing between these NSCLC cell lines that was independent of p53 status. TTFields treatment increased the G2/M population, with a concomitant reduction in S-phase cells followed by the appearance of a sub-G1 population indicative of apoptosis. Temporal changes in gene expression during TTFields exposure was evaluated to identify molecular signaling changes underlying the differential TTFields response. The most differentially expressed genes were associated with the cell cycle and cell proliferation pathways. However, the expression of genes found within the BRCA1 DNA-damage response were significantly downregulated (P<0.05) during TTFields treatment. DNA double-strand break (DSB) repair foci increased when cells were exposed to TTFields as did the appearance of chromatid-type aberrations, suggesting an interphase mechanism responsible for cell death involving DNA repair. Exposing cells to TTFields immediately following ionizing radiation resulted in increased chromatid aberrations and a reduced capacity to repair DNA DSBs, which were likely responsible for at least a portion of the enhanced cell killing seen with the combination. These findings suggest that TTFields induce a state of ‘BRCAness' leading to a conditional susceptibility resulting in enhanced sensitivity to ionizing radiation and provides a strong rationale for the use of TTFields as a combined modality therapy with radiation or other DNA-damaging agents.

Nonsteroidal anti-inflammatory drugs modulate cellular glycosaminoglycan synthesis by affecting EGFR and PI3K signaling pathways

In this report, selected non-steroidal anti-inflammatory drugs (NSAIDs), indomethacin and nimesulide, and analgesics acetaminophen, alone, as well as in combination with isoflavone genistein as potential glycosaminoglycan (GAG) metabolism modulators were considered for the treatment of mucopolysaccharidoses (MPSs) with neurological symptoms due to the effective blood-brain barrier (BBB) penetration properties of these compounds. We found that indomethacin and nimesulide, but not acetaminophen, inhibited GAG synthesis in fibroblasts significantly, while the most pronounced impairment of glycosaminoglycan production was observed after exposure to the mixture of nimesulide and genistein. Phosphorylation of the EGF receptor (EGFR) was inhibited even more effective in the presence of indomethacin and nimesulide than in the presence of genistein. When examined the activity of phosphatidylinositol-3-kinase (PI3K) production, we observed its most significant decrease in the case of fibroblast exposition to nimesulide, and afterwards to indomethacin and genistein mix, rather than indomethacin used alone. Some effects on expression of individual GAG metabolism-related and lysosomal function genes, and significant activity modulation of a number of genes involved in intracellular signal transduction pathways and metabolism of DNA and proteins were detected. This study documents that NSAIDs, and their mixtures with genistein modulate cellular glycosaminoglycan synthesis by affecting EGFR and PI3K signaling pathways.

Therapeutic effects of Qian-Yu decoction and its three extracts on carrageenan-induced chronic prostatitis/chronic pelvic pain syndrome in rats

BACKGROUND

Qian-Yu decoction (QYD) is a traditional Chinese medicinal recipe composed of Radix astragali (Astragalus membranaceus (Fisch.) Bunge var. mongholicus (Bunge) P.K. Hsiao, Fabaceae ), Herba epimedii (Epimedium brevicornum Maxim., Berberidaceae), Herba leonuri (Leonurus japonicus Houtt., Lamiaceae), Cortex phellodendri (Phellodendron chinense Schneid., Rutaceae) and Radix achyranthis bidentatae (Achyranthes bidentata Bl., Amaranthaceae). This study aimed to evaluate the therapeutic activity of QYD against carrageenan-induced chronic prostatic/chronic pelvic pain syndrome (CP/CPPS) in rats and further elucidate its effective components.

METHODS

Three types of components, total polysaccharides, total flavonoids and total saponins were separately extracted from QYD. Carrageenan-induced CP/CPPS rats were intragastrically administered with lyophilized product of QYD, individual extracts and all the combined forms of extracts for three weeks. Prostatic index (PI) was determined and histopathological analysis was performed. The levels of tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), cyclooxygenase-2 (COX-2) and prostaglandin E2 (PEG2) in rat prostate tissues were measured using ELISA. The production of inducible nitric oxide synthase (iNOS) was evaluated by an enzymatic activity assay, and the release of nitric oxide (NO) was determined by a nitrate/nitrite assay.

RESULTS

Treatment with QYD significantly ameliorated the histological changes of CP/CPPS rats and reduced the PI by 44.3%, with a marked downregulation of TNF-α (42.8% reduction), IL-1β (45.3%), COX-2 (36.6%), PGE2 (44.2%), iNOS (54.1%) and NO (46.0%). Each of three extracts attenuated the symptom of CP/CPPS, but much more weakly than QYD. The combined administration of three extracts showed efficacy comparable to that of QYD while better than that of any combination of two extracts. A principal component analysis of the six inflammatory mediators as variables indicated that the effects of TS on CP/CPPS were rather different from those of TF and TP, which were similar.

CONCLUSIONS

QYD can be beneficial in prevention and treatment of CP/CPPS. Polysaccharides, flavonoids and saponins, as the major effective components of QYD, exert a cooperative effect on CP/CPPS.

Ghrelin, CCK, GLP-1, and PYY(3-36): Secretory Controls and Physiological Roles in Eating and Glycemia in Health, Obesity, and After RYGB

The efficacy of Roux-en-Y gastric-bypass (RYGB) and other bariatric surgeries in the management of obesity and type 2 diabetes mellitus and novel developments in gastrointestinal (GI) endocrinology have renewed interest in the roles of GI hormones in the control of eating, meal-related glycemia, and obesity. Here we review the nutrient-sensing mechanisms that control the secretion of four of these hormones, ghrelin, cholecystokinin (CCK), glucagon-like peptide-1 (GLP-1), and peptide tyrosine tyrosine [PYY(3-36)], and their contributions to the controls of GI motor function, food intake, and meal-related increases in glycemia in healthy-weight and obese persons, as well as in RYGB patients. Their physiological roles as classical endocrine and as locally acting signals are discussed. Gastric emptying, the detection of specific digestive products by small intestinal enteroendocrine cells, and synergistic interactions among different GI loci all contribute to the secretion of ghrelin, CCK, GLP-1, and PYY(3-36). While CCK has been fully established as an endogenous endocrine control of eating in healthy-weight persons, the roles of all four hormones in eating in obese persons and following RYGB are uncertain. Similarly, only GLP-1 clearly contributes to the endocrine control of meal-related glycemia. It is likely that local signaling is involved in these hormones' actions, but methods to determine the physiological status of local signaling effects are lacking. Further research and fresh approaches are required to better understand ghrelin, CCK, GLP-1, and PYY(3-36) physiology; their roles in obesity and bariatric surgery; and their therapeutic potentials.

Evaluation of antiviral drug synergy in an infectious HCV system

BACKGROUND

Direct-acting antivirals (DAAs) have greatly improved the treatment of HCV infection. To improve response and prevent resistance, combination regimens have been the focus of clinical development. Regimens are often first assessed in vitro, with most combination studies to date using subgenomic replicon systems, which do not replicate the complete HCV life cycle and preclude study of entry and assembly inhibitors. Infectious full-length HCV systems have been developed and are being used to test drug efficacy.

METHODS

Using cell-based HCV Con1b replicon and an infectious full-length HCV (HCVcc-Luc) infection system, we systematically tested the synergy, additivity or antagonism of combinations of protease, NS5A and nucleotide NS5B inhibitor classes as well as the combination of these DAAs with host-targeting agent cyclosporin A or non-antibody entry inhibitor (S)-chlorcyclizine. Two computational software packages, MacSynergyII and CalcuSyn, were used for data analysis.

RESULTS

Combinations between different classes showed good consistency across the two viral assay systems and two software platforms. Combinations between NS5A and nucleotide NS5B inhibitors were synergistic, while combinations of protease inhibitors with the other two classes were additive to slightly antagonistic. As expected, combinations of antivirals of the same class were additive. Combination studies between these DAA classes and cyclosporin A or (S)-chlorcyclizine demonstrated additive to synergistic effects and highly synergistic effects, respectively. Combinations of these drugs did not show any added or unexpected cytotoxicity.

CONCLUSIONS

Our results show that in vitro combination studies of anti-HCV DAAs in the HCVcc system may provide useful guidance for drug combination designs in clinical studies. We also demonstrate that these DAAs in combination with host-targeting agents or entry inhibitors may improve HCV treatment response.

Combination of acamprosate and baclofen as a promising therapeutic approach for Parkinson's disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised by the loss of dopaminergic nigrostriatal neurons but which involves the loss of additional neurotransmitter pathways. Mono- or polytherapeutic interventions in PD patients have declining efficacy long-term and no influence on disease progression. The systematic analysis of available genetic and functional data as well as the substantial overlap between Alzheimer’s disease (AD) and PD features led us to repurpose and explore the effectiveness of a combination therapy (ABC) with two drugs – acamprosate and baclofen – that was already effective in AD animal models, for the treatment of PD. We showed in vitro that ABC strongly and synergistically protected neuronal cells from oxidative stress in the oxygen and glucose deprivation model, as well as dopaminergic neurons from cell death in the 6-hydroxydopamine (6-OHDA) rat model. Furthermore, we showed that ABC normalised altered motor symptoms in vivo in 6-OHDA-treated rats, acting by protecting dopaminergic cell bodies and their striatal terminals. Interestingly, ABC also restored a normal behaviour pattern in lesioned rats suggesting a symptomatic effect, and did not negatively interact with L-dopa. Our results demonstrate the potential value of combining repurposed drugs as a promising new strategy to treat this debilitating disease.

Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives

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.

Intestinal GLP-1 and satiation: from man to rodents and back

In response to luminal food stimuli during meals, enteroendocrine cells release gastrointestinal (GI) peptides that have long been known to control secretory and motor functions of the gut, pancreas and liver. Glucagon-like peptide-1 (GLP-1) has emerged as one of the most important GI peptides because of a combination of functions not previously ascribed to any other molecule. GLP-1 potentiates glucose-induced insulin secretion, suppresses glucagon release, slows gastric emptying and may serve as a satiation signal, although the physiological status of the latter function has not been fully established yet. Here we review the available evidence for intestinal GLP-1 to fulfill a number of established empirical criteria for assessing whether a hormone inhibits eating by eliciting physiological satiation in man and rodents.

Unlocking the Potential of High-Throughput Drug Combination Assays Using Acoustic Dispensing

Assessment of synergistic effects of drug combinations in vitro is a critical part of anticancer drug research. However, the complexities of dosing and analyzing two drugs over the appropriate range of doses have generally led to compromises in experimental design that restrict the quality and robustness of the data. In particular, the use of a single dose response of combined drugs, rather than a full two-way matrix of varying doses, has predominated in higher-throughput studies. Acoustic dispensing unlocks the potential of high-throughput dose matrix analysis. We have developed acoustic dispensing protocols that enable compound synergy assays in a 384-well format. This experimental design is considerably more efficient and flexible with respect to time, reagent usage, and labware than is achievable using traditional serial-dilution approaches. Data analysis tools integrated in Genedata Screener were used to efficiently deconvolute the combination compound mapping scheme and calculate compound potency and synergy metrics. We have applied this workflow to evaluate interactions among drugs targeting different nodes of the mitogen-activated protein kinase pathway in a panel of cancer cell lines.

Analysis of drug combinations: current methodological landscape

Combination therapies exploit the chances for better efficacy, decreased toxicity, and reduced development of drug resistance and owing to these advantages, have become a standard for the treatment of several diseases and continue to represent a promising approach in indications of unmet medical need. In this context, studying the effects of a combination of drugs in order to provide evidence of a significant superiority compared to the single agents is of particular interest. Research in this field has resulted in a large number of papers and revealed several issues. Here, we propose an overview of the current methodological landscape concerning the study of combination effects. First, we aim to provide the minimal set of mathematical and pharmacological concepts necessary to understand the most commonly used approaches, divided into effect-based approaches and dose-effect-based approaches, and introduced in light of their respective practical advantages and limitations. Then, we discuss six main common methodological issues that scientists have to face at each step of the development of new combination therapies. In particular, in the absence of a reference methodology suitable for all biomedical situations, the analysis of drug combinations should benefit from a collective, appropriate, and rigorous application of the concepts and methods reviewed here.

Combinatorial code governing cellular responses to complex stimuli

Cells adapt to their environment through the integration of complex signals. Multiple signals can induce synergistic or antagonistic interactions, currently considered as homogenous behaviours. Here, we use a systematic theoretical approach to enumerate the possible interaction profiles for outputs measured in the conditions 0 (control), signals X, Y, X+Y. Combinatorial analysis reveals 82 possible interaction profiles, which we biologically and mathematically grouped into five positive and five negative interaction modes. To experimentally validate their use in living cells, we apply an original computational workflow to transcriptomics data of innate immune cells integrating physiopathological signal combinations. Up to 9 of the 10 defined modes coexisted in context-dependent proportions. Each interaction mode was preferentially used in specific biological pathways, suggesting a functional role in the adaptation to multiple signals. Our work defines an exhaustive map of interaction modes for cells integrating pairs of physiopathological and pharmacological stimuli.

Cells constantly integrate information from multiple stimuli. By considering every possible means by which two stimuli can interact, Cappuccio et al. define 10 interaction modes and demonstrate their preferential use by dendritic cells responding to different combinations of microbial and host inflammatory cues.

Synergistic suppression of dengue virus replication using a combination of nucleoside analogs and nucleoside synthesis inhibitors

Dengue virus (DENV) is the most prevalent mosquito-borne viral pathogen in humans. Currently, there is no clinically approved vaccine or antiviral for DENV. Combination therapy is a common practice in antiviral treatment and a potential approach to search for new treatments for infectious pathogens. In this study, we performed a combination treatment in cell culture by using three distinct classes of inhibitors, including ribavirin (a guanosine analog with several antiviral mechanisms), brequinar (a pyrimidine biosynthesis inhibitor), and INX-08189 (a guanosine analog). The compound pairs were evaluated for antiviral activity by use of a DENV-2 luciferase replicon assay. Our result indicated that the combination of ribavirin and INX-08189 exhibited strong antiviral synergy. This result suggests that synergy can be achieved with compound pairs in which one compound suppresses the synthesis of the nucleoside for which the other compound is a corresponding nucleoside analog. In addition, we found that treatment of cells with brequinar alone could activate interferon-stimulated response elements (ISREs); furthermore, brequinar and NITD-982 (another pyrimidine biosynthesis inhibitor) potentiated interferon-induced ISRE activation. Compared to treatment with brequinar, treatment of cells with ribavirin alone could also induce ISRE activation, but to a lesser extent; however, when cells were cotreated with ribavirin and beta interferon, ribavirin did not augment the interferon-induced ISRE activation.

Polytherapy with a combination of three repurposed drugs (PXT3003) down-regulates Pmp22 over-expression and improves myelination, axonal and functional parameters in models of CMT1A neuropathy

Charcot-Marie-Tooth disease type 1A (CMT1A) is the most common inherited sensory and motor peripheral neuropathy. It is caused by PMP22 overexpression which leads to defects of peripheral myelination, loss of long axons, and progressive impairment then disability. There is no treatment available despite observations that monotherapeutic interventions slow progression in rodent models. We thus hypothesized that a polytherapeutic approach using several drugs, previously approved for other diseases, could be beneficial by simultaneously targeting PMP22 and pathways important for myelination and axonal integrity. A combination of drugs for CMT1A polytherapy was chosen from a group of authorised drugs for unrelated diseases using a systems biology approach, followed by pharmacological safety considerations. Testing and proof of synergism of these drugs were performed in a co-culture model of DRG neurons and Schwann cells derived from a Pmp22 transgenic rat model of CMT1A. Their ability to lower Pmp22 mRNA in Schwann cells relative to house-keeping genes or to a second myelin transcript (Mpz) was assessed in a clonal cell line expressing these genes. Finally in vivo efficacy of the combination was tested in two models: CMT1A transgenic rats, and mice that recover from a nerve crush injury, a model to assess neuroprotection and regeneration. Combination of (RS)-baclofen, naltrexone hydrochloride and D-sorbitol, termed PXT3003, improved myelination in the Pmp22 transgenic co-culture cellular model, and moderately down-regulated Pmp22 mRNA expression in Schwannoma cells. In both in vitro systems, the combination of drugs was revealed to possess synergistic effects, which provided the rationale for in vivo clinical testing of rodent models. In Pmp22 transgenic CMT1A rats, PXT3003 down-regulated the Pmp22 to Mpz mRNA ratio, improved myelination of small fibres, increased nerve conduction and ameliorated the clinical phenotype. PXT3003 also improved axonal regeneration and remyelination in the murine nerve crush model. Based on these observations in preclinical models, a clinical trial of PTX3003 in CMT1A, a neglected orphan disease, is warranted. If the efficacy of PTX3003 is confirmed, rational polytherapy based on novel combinations of existing non-toxic drugs with pleiotropic effects may represent a promising approach for rapid drug development.

A physiological perspective on the neuroscience of eating

I present the thesis that 'being physiological,' i.e., analyzing eating under conditions that do not perturb, or minimally perturb, the organism's endogenous processes, should be a central goal of the neuroscience of eating. I describe my understanding of 'being physiological' based on [i] the central neural-network heuristic of CNS function that traces back to Cajal and Sherrington, [ii] research on one of the simpler problems in the neuroscience of eating, identification of endocrine signals that control eating. In this context I consider natural meals, physiological doses and ranges, and antagonist studies. Several examples involve CCK. Next I describe my view of the cutting edge in the molecular neuroscience of eating as it has evolved from the discovery of leptin signaling through the application of optogenetic and pharmacogenetic methods. Finally I describe some novel approaches that may advance the neuroscience of eating in the foreseeable future. I conclude that [i] the neuroscience of eating may soon be able to discern 'physiological' function in the operation of CNS networks mediating eating, [ii] the neuroscience of eating should capitalize on methods developed in other areas of neuroscience, e.g., improved methods to record and manipulate CNS function in behaving animals, identification of canonical regional circuits, use of population electrophysiology, etc., and [iii] subjective aspects of eating are crucial aspects of eating science, but remain beyond mechanistic understanding.