Understanding synergy

Cancer-specific epigenome identifies oncogenic hijacking by nuclear factor I family proteins for medulloblastoma progression

Normal cells coordinate proliferation and differentiation by precise tuning of gene expression based on the dynamic shifts of the epigenome throughout the developmental timeline. Although non-mutational epigenetic reprogramming is an emerging hallmark of cancer, the epigenomic shifts that occur during the transition from normal to malignant cells remain elusive. Here, we capture the epigenomic changes that occur during tumorigenesis in a prototypic embryonal brain tumor, medulloblastoma. By comparing the epigenomes of the different stages of transforming cells in mice, we identify nuclear factor I family of transcription factors, known to be cell fate determinants in development, as oncogenic regulators in the epigenomes of precancerous and cancerous cells. Furthermore, genetic and pharmacological inhibition of NFIB validated a crucial role of this transcription factor by disrupting the cancer epigenome in medulloblastoma. Thus, this study exemplifies how epigenomic changes contribute to tumorigenesis via non-mutational mechanisms involving developmental transcription factors.

State-of-the-Art Review on Botanical Hybrid Preparations in Phytomedicine and Phytotherapy Research: Background and Perspectives

BACKGROUND

Despite some evidence supporting the synergy concept, the commonly known assumption that combinations of several herbs in one formulation can have better efficacy due to additive or synergistic effects has yet to be unambiguously and explicitly studied.

STUDY AIM

The study aimed to reveal the molecular interactions in situ of host cells in response to botanical hybrid preparations (BHP) intervention and justify the benefits of implementing BHP in clinical practice.

RESULTS

This prospective literature review provides the results of recent clinical and network pharmacology studies of BHP of Rhodiola rosea L. (Arctic root) with other plants, including Withania somnifera (L.) Dunal (ashwagandha), (Camellia sinensis (L.) Kuntze (green tea), Eleutherococcus senticosus (Rupr. and Maxim.) Maxim. (eleuthero), Schisandra chinensis (Turcz.) Baill. (schisandra), Leuzea carthamoides (Willd.) DC., caffeine, Cordyceps militaris L., Ginkgo biloba L.(ginkgo), Actaea racemosa L. (black cohosh), Crocus sativus L. (saffron), and L-carnosine.

CONCLUSIONS

The most important finding from network pharmacology studies of BHP was the evidence supporting the synergistic interaction of BHP ingredients, revealing unexpected new pharmacological activities unique and specific to the new BHP. Some studies show the superior efficacy of BHP compared to mono-drugs. At the same time, some a priori-designed combinations can fail, presumably due to antagonistic interactions and crosstalk between molecular targets within the molecular networks involved in the cellular and overall response of organisms to the intervention. Network pharmacology studies help predict the results of studies aimed at discovering new indications and unpredicted adverse events.

Revisiting the Checkerboard to Inform Development of β-Lactam/β-Lactamase Inhibitor Combinations

A two-dimensional "checkerboard" array employing systematic titration (e.g., serial two-fold dilutions) is a well-established in vitro method for exploring the antibacterial effects of novel drug combinations. Minimum inhibitory concentrations (MICs) on the checkerboard are isoeffective points at which the antibiotic potency is the same. Representations of checkerboard MIC curves for a β-lactam and β-lactamase inhibitor combination are used in hypothetical "thought experiments" and reveal the ways in which current practices can be improved. Because different types of response (i.e., independence vs. additivity vs. one effective agent; interaction vs. noninteraction) produce different MIC curves, data from different strains/isolates should not be pooled indiscriminately, as the composition of a pooled dataset will influence any derived pharmacokinetic/pharmacodynamic (PK/PD) index. Because the β-lactamase inhibitor threshold concentration (CT) parameter is a function of the β-lactam partner dosing regimen, it is not possible to derive a universal PK/PD index target based on CT. Alternative susceptibility testing methods represent different planes through the checkerboard; a fixed ratio method is less prone to bias for all β-lactam and β-lactamase inhibitor combinations. Susceptibility test MICs will often not reflect the sensitivity of the strain/isolate to the β-lactamase inhibitor, so the use of these MICs to normalize PK/PD indices is inappropriate.

Cooperative induction of CXCL chemokines by inflammatory cytokines and oxidized phospholipids

Inflammation is initiated and driven by a mixture of mediators, which modify effects of each other. This study analysed in vitro pro-inflammatory activity of inflammatory cytokines (TNFα and IL-1β) in a combination with a lipid DAMP molecule, oxidized palmitoyl-arachidonoyl-phosphatidylcholine (OxPAPC). The study was performed on endothelial and monocytic cell lines. The cells were treated with different concentrations of TNFα or IL-1β, OxPAPC and their combinations, either in the presence or absence of drugs regulating inflammation. Pro-inflammatory effects of TNFα/IL-1β and OxPAPC were estimated by analysis of chemokines CXCL8, CXCL2 and CXCL3 by ELISA and RT-PCR. Toxicity was determined by analysis of metabolic activity. Statistical significance was estimated by ANOVA and Dunnett's test. OxPAPC was a much weaker chemokine inducer as compared to TNFα or IL-1β. However, OxPAPC and TNFα/IL-1β together induced effects that were significantly stronger than the arithmetical sum of individual effects. This cooperative action of OxPAPC and TNFα was reversed by inhibitors of p38 MAPK. We hypothesise that the boosting of TNFα and IL-1β effects by OxPAPC may be more pathologically important than the action of the lipid alone. Inhibitors of p38 MAPK may become a tool for analysis of pathological role of oxidized phospholipids.

Challenges of bacteriophages application in controlling bacterial plant diseases and how to overcome them

Due to the emergence of antibiotic-resistant bacteria in agricultural sector, controlling bacterial plant diseases using antibiotics has become challenging. Researchers have turned to alternative methods, such as using bacteriophages as a biocide for plants instead of antibiotics, to control pathogenic bacterial plant diseases. However, the application of bacteriophages as a biocide in agriculture faces several challenges that may impede its success. In this review article, we discuss the various issues that could lead to the failure of its application. We also propose solutions to address each problem to increase awareness and familiarity before implementing the method to better ensure its success.

Tretinoin Enhances the Effects of Chemotherapy in Juvenile Myelomonocytic Leukemia Using an Ex Vivo Drug Sensitivity Assay

PURPOSE

Juvenile myelomonocytic leukemia (JMML) is an aggressive pediatric malignancy with myelodysplastic and myeloproliferative features. Curative treatment is restricted to hematopoietic stem-cell transplantation. Fludarabine combined with cytarabine (FLA) and 5-azacitidine (AZA) monotherapy are commonly used pre-transplant therapies. Here, we present a drug screening strategy using a flow cytometry-based precision medicine platform to identify potential additional therapeutic vulnerabilities.

METHODS

We screened 120 dual- and 10 triple-drug combinations (DCs) on peripheral blood (n = 21) or bone marrow (n = 6) samples from 27 children with JMML to identify DCs more effectively reducing leukemic cells than the DCs' components on their own. If fewer leukemic cells survived a DC ex vivo treatment compared with that DC's most effective component alone, the drug effect was referred to as cooperative. The difference between the two resistant fractions is the effect size.

RESULTS

We identified 26 dual- and one triple-DC more effective than their components. The differentiation agent tretinoin (TRET; all-trans retinoic acid) reduced the resistant fraction of FLA in 19/21 (90%) samples (decrease from 15% [2%-61%] to 11% [2%-50%] with a mean effect size of 3.8% [0.5%-11%]), and of AZA in 19/25 (76%) samples (decrease from 69% [34%-100+%] to 47% [17%-83%] with a mean effect size of 16% [0.3%-40%]). Among the resistant fractions, the mean proportion of CD38+ cells increased from 7% (0.03%-25%; FLA) to 17% (0.3%-38%; FLA + TRET) or from 10% (0.2%-31%; AZA) to 51% (0.8%-88%; AZA + TRET).

CONCLUSION

TRET enhanced the effects of FLA and AZA in ex vivo assays with primary JMML samples.

Constitutively Synergistic Multiagent Drug Formulations Targeting MERTK, FLT3, and BCL-2 for Treatment of AML

PURPOSE

Although high-dose, multiagent chemotherapy has improved leukemia survival rates, treatment outcomes remain poor in high-risk subsets, including acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) in infants. The development of new, more effective therapies for these patients is therefore an urgent, unmet clinical need.

METHODS

The dual MERTK/FLT3 inhibitor MRX-2843 and BCL-2 family protein inhibitors were screened in high-throughput against a panel of AML and MLL-rearranged precursor B-cell ALL (infant ALL) cell lines. A neural network model was built to correlate ratiometric drug synergy and target gene expression. Drugs were loaded into liposomal nanocarriers to assess primary AML cell responses.

RESULTS

MRX-2843 synergized with venetoclax to reduce AML cell density in vitro. A neural network classifier based on drug exposure and target gene expression predicted drug synergy and growth inhibition in AML with high accuracy. Combination monovalent liposomal drug formulations delivered defined drug ratios intracellularly and recapitulated synergistic drug activity. The magnitude and frequency of synergistic responses were both maintained and improved following drug formulation in a genotypically diverse set of primary AML bone marrow specimens.

CONCLUSIONS

We developed a nanoscale combination drug formulation that exploits ectopic expression of MERTK tyrosine kinase and dependency on BCL-2 family proteins for leukemia cell survival in pediatric AML and infant ALL cells. We demonstrate ratiometric drug delivery and synergistic cell killing in AML, a result achieved by a systematic, generalizable approach of combination drug screening and nanoscale formulation that may be extended to other drug pairs or diseases in the future.

Mathematical modeling of Escherichia coli O157:H7 growth in carrot juice influenced by Thymbra capitata essential oil, heat treatment, and storage temperature

The aim of this study was to develop a mathematical model describing the survival of Escherichia coli O157:H7 in carrot juice treated with Thymbra capitata essential oil combined with mild heat treatment and stored at different temperatures. The viable count method was used to investigate the effect of the treatment on bacterial survival, and the response surface methodology was used to develop a statistical model fitting the data. The results showed that the variance of bacterial growth is explained by storage temperature (37 %) and heat treatment (35 %), these are followed by Thymbra capitata essential oil (18 %) and their interaction (9 %). Positive multiplicative interaction was obtained for any pair of the studied treatments and cooperative effect synergy was observed over a large domain of these factors. A mathematical model was successfully developed to describe Escherichia coli O157:H7 response to the selected factors, within the study limits, and to estimate the risk of juice contamination and shelf-life. Based on our results, the use of Thymbra capitata essential oil combined with heat treatment may control Escherichia coli O157:H7 growth in carrot juice stored at low temperature.

Drug Combination Modeling: Methods and Applications in Drug Development

Combination therapies have become increasingly researched and used in the treatment and management of complex diseases due to their ability to increase the chances for better efficacy and decreased toxicity. To evaluate drug combinations in drug development, pharmacokinetic and pharmacodynamic interactions between drugs in combination can be quantified using mathematical models; however, it can be difficult to deduce which models to use and how to use them to aid in clinical trial simulations to simulate the effect of a drug combination. This review paper aims to provide an overview of the various methods used to evaluate combination drug interaction for use in clinical trial development and a practical guideline on how combination modeling can be used in the settings of clinical trials.

Latent variable indirect response modeling of clinical efficacy endpoints with combination therapy: application to guselkumab and golimumab in patients with ulcerative colitis

Accurate characterization of longitudinal exposure-response of clinical trial endpoints is important in optimizing dose and dosing regimens in drug development. Clinical endpoints are often categorical, for which much progress has been made recently in latent variable indirect response (IDR) modeling with single drugs. However, such applications have not yet been used for trials employing multiple drugs administered concurrently. This study aims to demonstrate that the latent variable IDR approach provides a convenient longitudinal exposure-response modeling framework to assess potential interaction effects of combination therapies. This is illustrated by an application to the exposure-response modeling of guselkumab, a monoclonal antibody in clinical development that blocks the interleukin-23p19 subunit, and golimumab, a monoclonal antibody that binds with high affinity to tumor necrosis factor-alpha. A Phase 2a study was conducted in 214 patients with moderate-to severe active ulcerative colitis for which longitudinal assessments of disease severity based on patient-reported measures of rectal bleeding, stool frequency, and symptomatic remission were evaluated as categorical endpoints, and fecal calprotectin as a continuous endpoint. The modeling results suggested independent pharmacodynamic guselkumab and golimumab effects on fecal calprotectin as a continuous endpoint, as well as interaction effects on the categorical endpoints that may be explained by an additional pathway of competitive interaction.

Screening of predicted synergistic multi-target therapies in glioblastoma identifies new treatment strategies

Background

IDH-wildtype glioblastoma (GBM) is a highly malignant primary brain tumor with a median survival of 15 months after standard of care, which highlights the need for improved therapy. Personalized combination therapy has shown to be successful in many other tumor types and could be beneficial for GBM patients.

Methods

We performed the largest drug combination screen to date in GBM, using a high-throughput effort where we selected 90 drug combinations for their activity onto 25 patient-derived GBM cultures. 43 drug combinations were selected for interaction analysis based on their monotherapy efficacy and were tested in a short-term (3 days) as well as long-term (18 days) assay. Synergy was assessed using dose-equivalence and multiplicative survival metrics.

Results

We observed a consistent synergistic interaction for 15 out of 43 drug combinations on patient-derived GBM cultures. From these combinations, 11 out of 15 drug combinations showed a longitudinal synergistic effect on GBM cultures. The highest synergies were observed in the drug combinations Lapatinib with Thapsigargin and Lapatinib with Obatoclax Mesylate, both targeting epidermal growth factor receptor and affecting the apoptosis pathway. To further elaborate on the apoptosis cascade, we investigated other, more clinically relevant, apoptosis inducers and observed a strong synergistic effect while combining Venetoclax (BCL targeting) and AZD5991 (MCL1 targeting).

Conclusions

Overall, we have identified via a high-throughput drug screening several new treatment strategies for GBM. Moreover, an exceptionally strong synergistic interaction was discovered between kinase targeting and apoptosis induction which is suitable for further clinical evaluation as multi-targeted combination therapy.

Synergy analysis with extended isobolography and its conceptual basis for the evaluation of antimicrobial effects of combined physical and chemical treatments

To evaluate the synergistic effects of physical and chemical antimicrobial treatments, whether for cidal or static effects, I proposed the extended isobologram (EIBo) analysis method, which is a modification of the isobologram (IBo) analysis commonly used to evaluate synergy between drugs. As the method types for this analysis, the growth delay (GD) assay reported previously by the author was introduced, in addition to the conventional endpoint (EP) assay. The evaluation analysis consists of five stages, analytical procedure establishment, antimicrobial activity assay, dose-effect analysis, IBo analysis, and synergy analysis. In EIBo analysis, the fractional antimicrobial dose (FAD) is introduced for the normalization of the antimicrobial activity of each treatment. For the synergy evaluation, the synergy parameter (SP) is defined as a measure of the degree of the synergistic effect of a combined treatment. This method makes it possible to quantitatively evaluate, predict, and compare various combination treatments as a hurdle technology.

Identifying an Optimal Neuroinflammation Treatment Using a Nanoligomer Discovery Engine

Acute activation of innate immune response in the brain, or neuroinflammation, protects this vital organ from a range of external pathogens and promotes healing after traumatic brain injury. However, chronic neuroinflammation leading to the activation of immune cells like microglia and astrocytes causes damage to the nervous tissue, and it is causally linked to a range of neurodegenerative diseases such as Alzheimer's diseases (AD), Multiple Sclerosis (MS), Parkinson's disease (PD), and many others. While neuroinflammation is a key target for a range of neuropathological diseases, there is a lack of effective countermeasures to tackle it, and existing experimental therapies require fairly invasive intracerebral and intrathecal delivery due to difficulty associated with the therapeutic crossover between the blood-brain barrier, making such treatments impractical to treat neuroinflammation long-term. Here, we present the development of an optimal neurotherapeutic using our Nanoligomer Discovery Engine, by screening downregulation of several proinflammatory cytokines (e.g., Interleukin-1β or IL-1β, tumor necrosis factor-alpha or TNF-α, TNF receptor 1 or TNFR1, Interleukin 6 or IL-6), inflammasomes (e.g., NLRP1), key transcription factors (e.g., nuclear factor kappa-B or NF-κβ) and their combinations, as upstream regulators and canonical pathway targets, to identify and validate the best-in-class treatment. Using our high-throughput drug discovery, target validation, and lead molecule identification via a bioinformatics and artificial intelligence-based ranking method to design sequence-specific peptide molecules to up- or downregulate gene expression of the targeted gene at will, we used our discovery engine to perturb and identify most effective upstream regulators and canonical pathways for therapeutic intervention to reverse neuroinflammation. The lead neurotherapeutic was a combination of Nanoligomers targeted to NF-κβ (SB.201.17D.8_NF-κβ1) and TNFR1 (SB.201.18D.6_TNFR1), which were identified using in vitro cell-based screening in donor-derived human astrocytes and further validated in vivo using a mouse model of lipopolysaccharide (LPS)-induced neuroinflammation. The combination treatment SB_NI_111 was delivered without any special formulation using a simple intraperitoneal injection of low dose (5 mg/kg) and was found to significantly suppress the expression of LPS-induced neuroinflammation in mouse hippocampus. These results point to the broader applicability of this approach towards the development of therapies for chronic neuroinflammation-linked neurodegenerative diseases, sleep countermeasures, and others, and the potential for further investigation of the lead neurotherapeutic molecule as reversible gene therapy.

Allosteric modulators of the δ GABAA receptor subtype demonstrate a therapeutic effect in morphine-antinociceptive tolerance and withdrawal in mice

The present study evaluated the effects of compounds targeting extrasynaptic δ subunit-containing γ-aminobutyric acid type A receptors (δ*-GABAARs) to interrogate the role of tonic inhibition in the development of antinociceptive tolerance caused by repeated morphine administration. We investigated the effect of subchronic or acute treatment with non-steroidal positive allosteric modulators (PAMs) of δ*-GABAARs, such as 2-261, on the morphine-antinociceptive tolerance. Mice were treated twice daily with morphine for 9 days and antinociception was measured using the hot water tail immersion test. Co-treatment with 2-261 and morphine prevented morphine-antinociceptive tolerance and acute administration of 2-261 on day 9 was sufficient to reverse the tolerance. Other compounds with activity at δ*-GABAARs also reversed morphine tolerance, whereas an enaminone that lacked activity at δ*-GABAARs did not. Acute administration of 2-261 did not cause an additive or synergistic antinociceptive effect when combined with an acute submaximal dose of morphine. We then used Cre/LoxP recombination to generate GABAA δ-subunit knockout mice to corroborate the pharmacological results. Observations of male δ-knockout mice demonstrated that the δ*-GABAARs was necessary for 2-261 modulation of both analgesic tolerance and somatic withdrawal symptoms produced by subchronic morphine. While female mice still benefited from the positive effects of 2-261, the δ-subunit was not necessary for these effects, highlighting a distinction of the different pathways that could have implications for some of the sex-related differences seen in human opioid-induced outcomes. Consequently, subtype-specific allosteric modulators of GABAARs may warrant further investigation as pharmacological targets to manage tolerance and withdrawal from opioids.

Dynamical Synergy of Drug Combinations during Cancer Chemotherapy

Synergistic drug combinations often provide effective strategies to increase treatment efficacy and, during therapy, it is a time-dependent process. Data for colorectal and lung cancer in vivo were used for the phenomenological study of dynamical synergy during treatments. The proposed approach takes into consideration tumor regrowth by macroscopic laws. The time dependencies of synergistic drug combinations are analyzed by different parametric indicators. The cumulative effects of the single therapy and drug combinations are quantitatively well described and related to the cumulative doses. In conclusion, the analysis of dynamical synergy during chemotherapy has to take into account the effects of the drug doses and the tumor regrowth, which can provide a reliable description of the synergistic time dependence.

A network paradigm predicts drug synergistic effects using downstream protein-protein interactions

In some cases, drug combinations affect adverse outcome phenotypes by binding the same protein; however, drug-binding proteins are associated through protein-protein interaction (PPI) networks within the cell, suggesting that drug phenotypes may result from long-range network effects. We first used PPI network analysis to classify drugs based on proteins downstream of their targets and next predicted drug combination effects where drugs shared network proteins but had distinct binding proteins (e.g., targets, enzymes, or transporters). By classifying drugs using their downstream proteins, we had an 80.7% sensitivity for predicting rare drug combination effects documented in gold-standard datasets. We further measured the effect of predicted drug combinations on adverse outcome phenotypes using novel observational studies in the electronic health record. We tested predictions for 60 network-drug classes on seven adverse outcomes and measured changes in clinical outcomes for predicted combinations. These results demonstrate a novel paradigm for anticipating drug synergistic effects using proteins downstream of drug targets.

Boosting the Immune Response—Combining Local and Immune Therapy for Prostate Cancer Treatment

Due to its slow progression and susceptibility to radical forms of treatment, low-grade PC is associated with high overall survival (OS). With the clinical progression of PC, the therapy is becoming more complex. The immunosuppressive tumor microenvironment (TME) makes PC a difficult target for most immunotherapeutics. Its general immune resistance is established by e.g., immune evasion through Treg cells, synthesis of immunosuppressive mediators, and the defective expression of surface neoantigens. The success of sipuleucel-T in clinical trials initiated several other clinical studies that specifically target the immune escape of tumors and eliminate the immunosuppressive properties of the TME. In the settings of PC treatment, this can be commonly achieved with radiation therapy (RT). In addition, focal therapies usually applied for localized PC, such as high-intensity focused ultrasound (HIFU) therapy, cryotherapy, photodynamic therapy (PDT), and irreversible electroporation (IRE) were shown to boost the anti-cancer response. Nevertheless, the present guidelines restrict their application to the context of a clinical trial or a prospective cohort study. This review explains how RT and focal therapies enhance the immune response. We also provide data supporting the combination of RT and focal treatments with immune therapies.

CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts

Anticancer combination therapy has been developed to increase efficacy by enhancing synergy. Patient-derived xenografts (PDXs) have emerged as reliable preclinical models to develop effective treatments in translational cancer research. However, most PDX combination study designs focus on single dose levels, and dose-response surface models are not appropriate for testing synergism. We propose a comprehensive statistical framework to assess joint action of drug combinations from PDX tumor growth curve data. We provide various metrics and robust statistical inference procedures that locally (at a fixed time) and globally (across time) access combination effects under classical drug interaction models. Integrating genomic and pharmacological profiles in non-small-cell lung cancer (NSCLC), we have shown the utilities of combPDX in discovering effective therapeutic combinations and relevant biological mechanisms. We provide an interactive web server, combPDX ( https://licaih.shinyapps.io/CombPDX/ ), to analyze PDX tumor growth curve data and perform power analyses.

Modeling synergistic effects by using general Hill-type response surfaces describing drug interactions

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 to describe co-operative effects are currently in use, the Additive Dose (ADM) or concentration addition (CA) and the Multiplicative Survival (MSM) or independent action (IA) models. Recently we proposed an approach which describes 'zero-interaction' surfaces based on the only requirement that simultaneous administration of different drugs leads to Hill-type response surfaces, which are solutions of the underlying logistic differential equations. No further assumptions, neither on mechanisms of action nor on limitations of parameter combinations are required. This defines-and limits-the application range of our approach. Resting on the same principle, we extend this ansatz in the present paper in order to describe deviations from the reference surface by generalized Hill-type functions. To this end we introduce two types of parameters, perturbations of the pure drug Hill-parameters and interaction parameters that account for n-tuple interactions between all components of a mixture. The resulting 'full-interaction' response surface is a valid solution of the basic partial differential equation (PDE), satisfying appropriate boundary conditions. This is true irrespective of its actual functional form, as within our framework the number of parameters is not fixed. We start by fitting the experimental data to the 'full-interaction' model with the maximum possible number of parameters. Guided by the fit-statistics, we then gradually remove insignificant parameters until the optimum response surface model is obtained. The 'full-interaction' Hill response surface ansatz can be applied to mixtures of n compounds with arbitrary Hill parameters including those describing baseline effects. Synergy surfaces, i.e., differences between full- and null-interaction models, are used to identify dose-combinations showing peak synergies. We apply our approach to binary and ternary examples from the literature, which range from mixtures behaving according to the null-interaction model to those showing strong synergistic or antagonistic effects. By comparing 'null-' and 'full-response' surfaces we identify those dose-combinations that lead to maximum synergistic or antagonistic effects. In one example we identify both synergistic and antagonistic effects simlutaneously, depending on the dose-ratio of the components. In addition we show that often the number of parameters necessary to describe the response can be reduced without significantly affecting the accuracy. This facilitates an analysis of the synergistic effects by focussing on the main factors causing the deviations from 'null-interaction'.

Synergistic Anti-Inflammatory Activity of Ginger and Turmeric Extracts in Inhibiting Lipopolysaccharide and Interferon-γ-Induced Proinflammatory Mediators

This study aims to investigate the combined anti-inflammatory activity of ginger and turmeric extracts. By comparing the activities of individual and combined extracts in lipopolysaccharide and interferon-γ-induced murine RAW 264.7 cells, we demonstrated that ginger-turmeric combination was optimal at a specific ratio (5:2, w/w) in inhibiting nitric oxide, tumour necrosis factor and interleukin 6 with synergistic interaction (combination index < 1). The synergistic inhibitory effect on TNF was confirmed in human monocyte THP-1 cells. Ginger-turmeric combination (5:2, w/w) also upregulated nuclear factor erythroid 2−related factor 2 activity and heme oxygenase-1 protein expression. Additionally, 6-shogaol, 8-shogaol, 10-shogaol and curcumin were the leading compounds in reducing major proinflammatory mediators and cytokines, and a simplified compound combination of 6-s, 10-s and curcumin showed the greatest potency in reducing LPS-induced NO production. Our study provides scientific evidence in support of the combined use of ginger and turmeric to alleviate inflammatory processes.

OBIF: an omics-based interaction framework to reveal molecular drivers of synergy

Bioactive molecule library screening may empirically identify effective combination therapies, but molecular mechanisms underlying favorable drug–drug interactions often remain unclear, precluding further rational design. In the absence of an accepted systems theory to interrogate synergistic responses, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in synergistic biological responses. OBIF performs full factorial analysis of feature expression data from single versus dual exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed transcriptomic and proteomic data of a dyad of immunostimulatory molecules that induces synergistic protection against influenza A and revealed unanticipated NF-κB/AP-1 cooperation that is required for antiviral protection. To demonstrate generalizability, OBIF analyzed data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, unlike existing synergy quantification and prediction methods, OBIF is a phenotype-driven systems model that supports multiplatform interrogation of synergy mechanisms.

Role of the Synergistic Interactions of Environmental Pollutants in the Development of Cancer

There is a growing awareness that the large number of environmental pollutants we are exposed to on a daily basis are causing major health problems. Compared to traditional studies that focus on individual pollutants, there are relatively few studies on how pollutants mixtures interact. Several studies have reported a relationship between environmental pollutants and the development of cancer, even when pollutant levels are below toxicity reference values. The possibility of synergistic interactions between different pollutants could explain how even low concentrations can cause major health problems. These intricate that molecular interactions can occur through a wide variety of mechanisms, and our understanding of the physiological effects of mixtures is still limited. The purpose of this paper is to discuss recent reports that address possible synergistic interactions between different types of environmental pollutants that could promote cancer development. Our literature studies suggest that key biological pathways are frequently implicated in such processes. These include increased production of reactive oxygen species, activation by cytochrome P450, and aryl hydrocarbon receptor signaling, among others. We discuss the need to understand individual pathological vulnerability not only in relation to basic genetics and gene expression, but also in terms of measurable exposure to contaminants. We also mention the need for significant improvements in future studies using a multitude of disciplines, such as the development of high-throughput study models, better tools for quantifying pollutants in cancer patients, innovative pharmacological and toxicological studies, and high-efficiency computer analysis, which allow us to analyze the molecular mechanisms of mixtures.

Bidirectional Responses of Eight Neuroinflammation-Related Transcriptional Factors to 64 Flavonoids in Astrocytes with Transposable Insulated Signaling Pathway Reporters

Neuroinflammation is implicated in a variety of pathologies and is mechanistically linked to hyperactivation of glial cells in the central nervous system (CNS), predominantly in response to external stimuli. Multiple dietary factors were reported to alter neuroinflammation, but their actions on the relevant transcription factors in glia are not sufficiently understood. Here, an in vitro protocol employing cultured astroglial cells, which carry reporters of multiple signaling pathways associated with inflammation, was developed for screening environmental factors and synthetic drugs. Immortalized rat astrocyte line DI TNC1 was stably transfected with piggyBac transposon vectors containing a series of insulated reporters for the transcriptional activity of NF-κB, AP-1, signal transducer and activator of transcription 1 (STAT1), signal transducer and activator of transcription 3 (STAT3), aromatic hydrocarbon receptor (AhR), Nrf2, peroxisome proliferator-activated receptor γ (PPARγ), and HIF-1α, which is quantified via luciferase assay. Concatenated green fluorescent protein (GFP) expression was employed for simultaneous evaluation of cellular viability. Responses to a set of 64 natural and synthetic monomeric flavonoids representing six main structural classes (flavan-3-ols, flavanones, flavones, flavonols, isoflavones, and anthocyan(id)ins) were obtained at 10 and 50 μM concentrations. Except for HIF-1α, the activity of NF-κB and other transcription factors (TFs) in astrocytes was predominantly inhibited by flavan-3-ols and anthocyan(id)ins, while flavones and isoflavones generally activated these TFs. In addition, we obtained dose-response profiles for 11 flavonoids (apigenin, baicalein, catechin, cyanidin, epigallocatechin gallate, genistein, hesperetin, kaempferol, luteolin, naringenin, and quercetin) within the 1-100 μM range and in the presence of immune-stimulants and immune-suppressors. The flavonoid concentration profiles for TF-activation reveal biphasic response curves from the astrocytes. Apart from epigallocatechin gallate (EGCG), flavonoids failed to inhibit the NF-κB activation by proinflammatory agents [lipopolysaccharide (LPS), cytokines], but most of the tested polyphenols synergized with STAT3 inhibitors (stattic, ruxolitinib) against the activation of this TF in the astrocytes. We conclude that transposable insulated reporters of transcriptional activation represent a convenient neurochemistry tool in screening for activators/inhibitors of signaling pathways.

Botanical Interventions to Improve Glucose Control and Options for Diabetes Therapy

Diabetes mellitus is a major public health problem worldwide. This endocrine disease is clustered into distinct subtypes based on the route of development, with the most common forms associated with either autoimmunity (T1DM) or obesity (T2DM). A shared hallmark of both major forms of diabetes is a reduction in function (insulin secretion) or mass (cell number) of the pancreatic islet beta-cell. Diminutions in both mass and function are often present. A wide assortment of plants have been used historically to reduce the pathological features associated with diabetes. In this review, we provide an organized viewpoint focused around the phytochemicals and herbal extracts investigated using various preclinical and clinical study designs. In some cases, crude extracts were examined directly, and in others, purified compounds were explored for their possible therapeutic efficacy. A subset of these studies compared the botanical product with standard of care prescribed drugs. Finally, we note that botanical formulations are likely suspects for future drug discovery and refinement into class(es) of compounds that have either direct or adjuvant therapeutic benefit.

Multi-component food-items and eating behaviour: What do we know and what do we need to know?

Multi-component food-items are single food products that comprise more than one food class, brought together usually via some form of processing. Importantly, individual components of the food-item remain discernible and sensorially distinguishable from each other (e.g., chocolate chip cookies or 'choc ice'). Despite a sizable research literature on the formulation of such products, there lacks a concomitant research literature on the effect(s) of multi-component food-items (compared to single component food-items) on eating behaviour. Considerable previous research has investigated the effect of multiple separate food items on food intake, portion size selection and palatability. However, studies rarely use test foods that capture the physical or chemical interactions between components that are characteristic of multi-component foods. Nevertheless, previous research and relevant theory allow us to generate hypotheses about how multi-component foods may affect eating behaviour; consideration of food variety and perceived sensory complexity suggest that consumption of multi-component foods are likely to increase perceived palatability of such foods, self-selected portion size and food intake. Moreover, many (but not all) multi-component foods would be considered ultra-processed, which is a driver of food intake in and of itself. One possibility is that food components brought together as part of a multi-component food-item interact to strongly drive eating behaviour. To explore this idea, researchers will need to work across disciplines to address various practical and methodological barriers including the technical preparation of test foods.

Paeonol assists fluconazole and amphotericin B to inhibit virulence factors and pathogenicity of Candida albicans

This study aimed to evaluate the mono- and dual- antifungal activities of paeonol (PAE) and fluconazole (FLZ)/amphotericin B (AmB). To this end, the effects of PAE and FLZ/AmB on cell surface hydrophobicity, hydrolase activity, morphological transition were investigated in vitro and in a Galleria mellonella infection model. The results showed a relatively high minimum inhibitory concentration (MIC) and sessile MIC (SMIC) of PAE alone. However, compared with the single drug, the combined use of PAE and FLZ/AmB had a potent synergistic potential to inhibit the virulence factors for Candida. The concomitant use of two drugs was consistently more effective than either drug alone for increasing survival rate, decreasing the fungal burden, and alleviating the pathological features of G. mellonella infected by the fungus. Taken together, these findings demonstrate the anti-Candida effects of PAE plus FLZ/AmB and their potential to increase the sensitivity of C. albicans to FLZ/AmB of PAE.

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

The lack of a unifying metric characterizing combinatorial drug interactions has impeded the development of combinatorial therapies. Here, the authors present MuSyC, a consensus synergy metric that overcomes several caveats associated with other, popular metrics.

Synergy in Nanomedicine: What It Is Not, and What It Might Be

Interactions between nanoformulations delivering two or more drugs-or a drug and some other therapeutic modality-are often described as synergistic. In fact, synergy is a specific term of art, with important therapeutic implications. The term is often misused in the nanoscience literature. Here we discuss what synergy is and an approach to demonstrating it rigorously.

Combinations of a full and partial agonist: Experimental evidence of curved isoboles

Concentration addition as a classic null model for toxicology and pharmacology is based on Loewe's mathematical formulation and the linearity of the isoboles. Novel mathematical models, however, propose curved isoboles in certain conditions. This article aims to test the hypothesis of the curvature of isoboles in experimental measurements. With the assumption of linear isoboles, a partial agonist acts as an antagonist above its maximal effect level. The isoboles automatically convert to a positive slope. For curved isoboles, a partial agonist acts as an antagonist at higher effect levels than its maximal effect alone. The discrepancies between effect levels were studied with an estrogen receptor binding assay (BMAEREluc/ERα) using a mixture of 17β-estradiol and fulvestrant as a partial agonist. A mixture of 17β-estradiol and fulvestrant acts as a partial agonist and causes the diminishing of the effect level of 17β-estradiol at a significantly higher level than the maximal effect of their partial-agonistic dose-response curve. Measured, elevated effect levels were well predicted by the mathematical model. Nonlinear isoboles may change our understanding and definition of synergism or antagonism and prompt further attention in receptor theory.

Solving the Problem of Assessing Synergy and Antagonism for Non-Traditional Dosing Curve Compounds Using the DE/ZI Method: Application to Nrf2 Activators

Multi-drug combination therapy carries significant promise for pharmacological intervention, primarily better efficacy with less toxicity and fewer side effects. However, the field lacks methodology to assess synergistic or antagonistic interactions for drugs with non-traditional dose response curves. Specifically, our goal was to assess small-molecule modulators of antioxidant response element (ARE)-driven gene expression, which is largely regulated by the Nrf2 transcription factor. Known as Nrf2 activators, this class of compounds upregulates a battery of cytoprotective genes and shows significant promise for prevention of numerous chronic diseases. For example, sulforaphane sourced from broccoli sprouts is the subject of over 70 clinical trials. Nrf2 activators generally have non-traditional dose response curves that are hormetic, or U-shaped. We introduce a method based on the principles of Loewe Additivity to assess synergism and antagonism for two compounds in combination. This method, termed Dose-Equivalence/Zero Interaction (DE/ZI), can be used with traditional Hill-slope response curves, and it also can assess interactions for compounds with non-traditional curves, using a nearest-neighbor approach. Using a Monte-Carlo method, DE/ZI generates a measure of synergy or antagonism for each dosing pair with an associated error and p-value, resulting in a 3D response surface. For the assessed Nrf2 activators, sulforaphane and di-tert-butylhydroquinone, this approach revealed synergistic interactions at higher dosing concentrations consistently across data sets and potential antagonistic interactions at lower concentrations. DE/ZI eliminates the need to determine the best fit equation for a given data set and values experimentally-derived results over formulated fits.

Drug combination therapy for emerging viral diseases

Effective therapeutics to combat emerging viral infections are an unmet need. Historically, treatments for chronic viral infections with single drugs have not been successful, as exemplified by human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infections. Combination therapy for these diseases has led to improved clinical outcomes with dramatic reductions in viral load, morbidity, and mortality. Drug combinations can enhance therapeutic efficacy through additive, and ideally synergistic, effects for emerging and re-emerging viruses, such as influenza, severe acute respiratory syndrome-coronavirus (SARS-CoV), Middle East respiratory syndrome (MERS)-CoV, Ebola, Zika, and SARS-coronavirus 2 (CoV-2). Although novel drug development through traditional pipelines remains a priority, in the interim, effective synergistic drug candidates could be rapidly identified by drug-repurposing screens, facilitating accelerated paths to clinical testing and potential emergency use authorizations.

Synergy across the drugs approved for the treatment of asthma

INTRODUCTION

Inhaled corticosteroids are the cornerstone for the treatment of stable asthma, however, when disease severity increases, escalating therapy to combinations of drugs acting on distinct signalling pathways is required. It is advantageous to providing evidence of a synergistic interaction across drug combinations, as it allows optimizing bronchodilation while lowering the dose of single agents. In the respiratory pharmacology field, two statistical models are accepted as gold standard to characterize drug interactions, namely the Bliss Independence criterion and the Unified Theory. In this review, pharmacological interactions across drugs approved for the treatment of asthma have been systematically assessed.

EVIDENCE ACQUISITION

A comprehensive literature search was performed in MEDLINE for studies that used a validated pharmacological method for assessing drug interaction. The results were extracted and reported via qualitative synthesis.

EVIDENCE SYNTHESIS

Overall, 45 studies were identified from literature search and 5 met the inclusion criteria. Current evidence coming from ex vivo models of asthma indicates that drug combinations modulating bronchial contractility induce a synergistic bronchorelaxant effect. In murine models of lung inflammation, the combination between inhaled corticosteroids and β2- adrenoceptor agonists synergistically improve lung function and the inflammatory profile.

CONCLUSIONS

There is still limited knowledge regarding the mechanistic basis underlying pharmacological interactions across drugs approved for asthma. The synergism elicited by combined agents is an effect of class. Specifically designed clinical trials are needed to confirm the results coming from preclinical evidence, but also to establish the minimal dose for combined agents to induce a synergistic interaction and maximize bronchodilation.

The prospects of tumor chemosensitivity testing at the single-cell level

Tumor chemosensitivity testing plays a pivotal role in the optimal selection of chemotherapeutic regimens for cancer patients in a personalized manner. High-throughput drug screening approaches have been developed but they failed to take into account intratumor heterogeneity and therefore only provided limited predictive power of therapeutic response to individual cancer patients. Single cancer cell drug sensitivity testing (SCC-DST) has been recently developed to evaluate the variable sensitivity of single cells to different anti-tumor drugs. In this review, we discuss how SCC-DST overcomes the obstacles of traditional drug screening methodologies. We outline critical procedures of SCC-DST responsible for single-cell generation and sorting, cell-drug encapsulation on a microfluidic chip and detection of cell-drug interactions. In SCC-DST, droplet-based microfluidics is emerging as an important platform that integrated various assays and analyses for drug susceptibility tests for individual patients. With the advancement of technology, both fluorescence imaging and label-free analysis have been used for detecting single cell-drug interactions. We also discuss the feasibility of integrating SCC-DST with single-cell RNA sequencing to unravel the mechanisms leading to drug resistance, and utilizing artificial intelligence to facilitate the analysis of various omics data in the evaluation of drug susceptibility. SCC-DST is setting the stage for better drug selection for individual cancer patients in the era of precision medicine.

The effect of Jiedu Huoxue decoction on rat model of experimental nonbacterial prostatitis via regulation of miRNAs

CONTEXT

The underlying mechanisms of Jiedu Huoxue decoction (JDHXD) in treating chronic prostatitis have not been fully explored.

OBJECTIVE

This study investigates the miRNAs as potential biomarkers and the effect of JDHXD on the rat model of experimental nonbacterial prostatitis.

MATERIALS AND METHODS

Fifty-four Sprague-Dawley male rats were randomly divided into normal control, model, JDHXD low dose (0.5 g/kg/day), medium dose (1 g/kg/day), high dose (2 g/kg/day) and western medicine (cernilton 0.094 g/kg/day) groups, and intragastrically administered once daily for 30 days. The control and model (upon successful establishment) groups received distilled water. Differential expression of miRNAs was analysed with high-throughput miRNA sequencing and validated with qRT-PCR and Northern blot. Prediction of specific target genes and functional enrichment analysis were performed with bioinformatics.

RESULTS

LD₅₀ test showed no sign of toxicity with maximum feasible dose 4 g/kg JDHXD. Compared with control, 495 miRNAs showed expression changes in CAP/CPPS rats, of which 211 were significantly different and 37 were prostatic-related. There were 181 differentially expressed miRNAs between the model and high dose JDHXD groups, of which 23 were identical with the control and model groups. Compared with control, miR-146a, miR-423 and miR-205 expression increased significantly in the model group, decreased dose-dependently in the JDHXD groups (p < 0.05), and vice-versa for miR-96 (p < 0.05). The effect of low dose JDHXD was comparable to cernilton (p > 0.05).

DISCUSSION AND CONCLUSIONS

Future studies may explore the contributions of the active components in JDHXD. The study design is generalisable. The effect can be repeatedly verified in clinical trials.

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

COMBSecretomics: A pragmatic methodological framework for higher-order drug combination analysis using secretomics

Multi drug treatments are increasingly used in the clinic to combat complex and co-occurring diseases. However, most drug combination discovery efforts today are mainly focused on anticancer therapy and rarely examine the potential of using more than two drugs simultaneously. Moreover, there is currently no reported methodology for performing second- and higher-order drug combination analysis of secretomic patterns, meaning protein concentration profiles released by the cells. Here, we introduce COMBSecretomics (https://github.com/EffieChantzi/COMBSecretomics.git), the first pragmatic methodological framework designed to search exhaustively for second- and higher-order mixtures of candidate treatments that can modify, or even reverse malfunctioning secretomic patterns of human cells. This framework comes with two novel model-free combination analysis methods; a tailor-made generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering. Quality control procedures to eliminate outliers and non-parametric statistics to quantify uncertainty in the results obtained are also included. COMBSecretomics is based on a standardized reproducible format and could be employed with any experimental platform that provides the required protein release data. Its practical use and functionality are demonstrated by means of a proof-of-principle pharmacological study related to cartilage degradation. COMBSecretomics is the first methodological framework reported to enable secretome-related second- and higher-order drug combination analysis. It could be used in drug discovery and development projects, clinical practice, as well as basic biological understanding of the largely unexplored changes in cell-cell communication that occurs due to disease and/or associated pharmacological treatment conditions.

Divergence and Convergence of Cerebral Ischemia Pathways Profile Deciphers Differential Pure Additive and Synergistic Mechanisms

Aim

The variable mechanisms on additive and synergistic effects of jasminoidin (JA)-Baicalin (BA) combination and JA-ursodeoxycholic acid (UA) combination in treating cerebral ischemia are not completely understood. In this study, we explored the differential pure mechanisms of additive and synergistic effects based on pathway analysis that excluded ineffective interference.

Methods

The MCAO mice were divided into eight groups: sham, vehicle, BA, JA, UA, Concha Margaritifera (CM), BA-JA combination (BJ), and JA-UA combination (JU). The additive and synergistic effects of combination groups were identified by cerebral infarct volume calculation. The differentially expressed genes based on a microarray chip containing 16,463 oligoclones were uploaded to GeneGo MetaCore software for pathway analyses and function catalogue. The comparison of specific pathways and functions crosstalk between different groups were analyzed to reveal the underlying additive and synergistic pharmacological variations.

Results

Additive BJ and synergistic JU were more effective than monotherapies of BA, JA, and UA, while CM was ineffective. Compared with monotherapies, 43 pathways and six functions were found uniquely in BJ group, with 33 pathways and three functions in JU group. We found six overlapping pathways and six overlapping functions between BJ and JU groups, which mainly involved central nervous system development. Thirty-seven specific pathways and 10 functions were activated by additive BJ, which were mainly related to cell adhesion and G-protein signaling; and 27 specific pathways and three functions of synergistic JU were associated with regulation of metabolism, DNA damage, and translation. The overlapping and distinct pathways and functions may contribute to different additive and synergistic effects.

Conclusion

The divergence pathways of pure additive effect of BJ were mainly related to cell adhesion and G-protein signaling, while the pure synergistic mechanism of JU depended on metabolism, translation and DNA damage. Such a systematic analysis of pathways may provide an important paradigm to reveal the pharmacological mechanisms underlying drug combinations.

Tumor treating fields cause replication stress and interfere with DNA replication fork maintenance: Implications for cancer therapy

Tumor treating fields (TTFields) is a noninvasive physical modality of cancer therapy that applies low-intensity, intermediate frequency, and alternating electric fields to a tumor. Interference with mitosis was the first mechanism describing the effects of TTFields on cancer cells; however, TTFields was shown to not only reduce the rejoining of radiation-induced DNA double-strand breaks (DSBs), but to also induce DNA DSBs. The mechanism(s) by which TTFields generates DNA DSBs is related to the generation of replication stress including reduced expression of the DNA replication complex genes MCM6 and MCM10 and the Fanconi's Anemia pathway genes. When markers of DNA replication stress as a result of TTFields exposure were examined, newly replicated DNA length was reduced with TTFields exposure time and there was increased R-loop formation. Furthermore, as cells were exposed to TTFields a conditional vulnerability environment developed which rendered cells more susceptible to DNA damaging agents or agents that interfere with DNA repair or replication fork maintenance. The effect of TTFields exposure with concomitant exposure to cisplatin or PARP inhibition, the combination of TTFields plus concomitant PARP inhibition followed by radiation, or radiation alone at the end of a TTFields exposure were all synergistic. Finally, gene expression analysis of 47 key mitosis regulator genes suggested that TTFields-induced mitotic aberrations and DNA damage/replication stress events, although intimately linked to one another, are likely initiated independently of one another. This suggests that enhanced replication stress and reduced DNA repair capacity are also major mechanisms of TTFields effects, effects for which there are therapeutic implications.

Assessing the Effect of Mycotoxin Combinations: Which Mathematical Model Is (the Most) Appropriate?

In the past decades, many studies have examined the nature of the interaction between mycotoxins in biological models classifying interaction effects as antagonisms, additive effects, or synergisms based on a comparison of the observed effect with the expected effect of combination. Among several described mathematical models, the arithmetic definition of additivity and factorial analysis of variance were the most commonly used in mycotoxicology. These models are incorrectly based on the assumption that mycotoxin dose-effect curves are linear. More appropriate mathematical models for assessing mycotoxin interactions include Bliss independence, Loewe’s additivity law, combination index, and isobologram analysis, Chou-Talalays median-effect approach, response surface, code for the identification of synergism numerically efficient (CISNE) and MixLow method. However, it seems that neither model is ideal. This review discusses the advantages and disadvantages of these mathematical models.

Phenotype-based probabilistic analysis of heterogeneous responses to cancer drugs and their combination efficacy

Cell-to-cell variability generates subpopulations of drug-tolerant cells that diminish the efficacy of cancer drugs. Efficacious combination therapies are thus needed to block drug-tolerant cells via minimizing the impact of heterogeneity. Probabilistic models such as Bliss independence have been developed to evaluate drug interactions and their combination efficacy based on probabilities of specific actions mediated by drugs individually and in combination. In practice, however, these models are often applied to conventional dose-response curves in which a normalized parameter with a value between zero and one, generally referred to as fraction of cells affected (fa), is used to evaluate the efficacy of drugs and their combined interactions. We use basic probability theory, computer simulations, time-lapse live cell microscopy, and single-cell analysis to show that fa metrics may bias our assessment of drug efficacy and combination effectiveness. This bias may be corrected when dynamic probabilities of drug-induced phenotypic events, i.e. induction of cell death and inhibition of division, at a single-cell level are used as metrics to assess drug efficacy. Probabilistic phenotype metrics offer the following three benefits. First, in contrast to the commonly used fa metrics, they directly represent probabilities of drug action in a cell population. Therefore, they deconvolve differential degrees of drug effect on tumor cell killing versus inhibition of cell division, which may not be correlated for many drugs. Second, they increase the sensitivity of short-term drug response assays to cell-to-cell heterogeneities and the presence of drug-tolerant subpopulations. Third, their probabilistic nature allows them to be used directly in unbiased evaluation of synergistic efficacy in drug combinations using probabilistic models such as Bliss independence. Altogether, we envision that probabilistic analysis of single-cell phenotypes complements currently available assays via improving our understanding of heterogeneity in drug response, thereby facilitating the discovery of more efficacious combination therapies to block drug-tolerant cells.

Delineation of cell death mechanisms induced by synergistic effects of statins and erlotinib in non-small cell lung cancer cell (NSCLC) lines

Hydroxymethylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors (statins) have been shown to overcome tyrosine kinase inhibitor (TKI) resistance in epithelial growth factor receptor (EGFR) mutated non-small cell lung cancer (NSCLC) cells in vivo and in vitro. However, little is known about the putative induction of non-apoptotic cell death pathways by statins. We investigated the effects of pitavastatin and fluvastatin alone or in combination with erlotinib in three NSCLC cell lines and examined the activation of different cell death pathways. We assessed apoptosis via fluorometric caspase assay and poly (ADP-ribose) polymerase 1 (PARP) cleavage. Furthermore, annexinV/propidium iodide (PI) flow cytometry was performed. Small molecule inhibitors benzyloxycarbonyl-Val-Ala-Asp-fluoromethyl ketone (zVAD), necrostatin 1 (Nec1), ferrostatin 1 (Fer1), Ac-Lys-Lys-Norleucinal (Calp1) were used to characterise cell death pathway(s) putatively (co-)activated by pitavastatin/erlotinib co-treatment. Synergism was calculated by additivity and isobolographic analyses. Pitavastatin and fluvastatin induced cell death in EGFR TKI resistant NSCLC cells lines A549, Calu6 and H1993 as shown by caspase 3 activation and PARP cleavage. Co-treatment of cells with pitavastatin and the EGFR TKI erlotinib resulted in synergistically enhanced cytotoxicity compared to pitavastatin monotherapy. Flow cytometry indicated the induction of alternative regulated cell death pathways. However, only co-treatment with mevalonic acid (Mev) or the pan-caspase inhibitor zVAD could restore cell viability. The results show that cytotoxicity mediated by statin/erlotinib co-treatment is synergistic and can overcome erlotinib resistance in K-ras mutated NSCLC and relies only on apoptosis.

IGF2 Autocrine-Mediated IGF1R Activation Is a Clinically Relevant Mechanism of Osimertinib Resistance in Lung Cancer

EGFR-mutated lung cancer accounts for a significant proportion of lung cancer cases worldwide. For these cases, osimertinib, a third-generation EGFR tyrosine kinase inhibitor, is extensively used as a first-line or second-line treatment. However, lung cancer cells acquire resistance to osimertinib in 1 to 2 years. Thus, a thorough clarification of resistance mechanisms to osimertinib is highly anticipated. Recent next-generation sequencing (NGS) of lung cancer samples identified several genetically defined resistance mechanisms to osimertinib, such as EGFR C797S or MET amplification. However, nongenetically defined mechanisms are not well evaluated. For a thorough clarification of osimertinib resistance, both genetic and nongenetic mechanisms are essential. By using our comprehensive protein phosphorylation array, we detected IGF1R bypass pathway activation after EGFR abolishment. Both of our established lung cancer cells and patient-derived lung cancer cells demonstrated IGF2 autocrine-mediated IGF1R pathway activation as a mechanism of osimertinib resistance. Notably, this resistance mechanism was not detected by a previously performed NGS, highlighting the essential roles of living cancer cells for a thorough clarification of resistance mechanisms. Interestingly, the immunohistochemical analysis confirmed the increased IGF2 expression in lung cancer patients who were treated with osimertinib and met the established clinical definition of acquired resistance. The findings highlight the crucial roles of cell-autonomous ligand expression in osimertinib resistance. Here, we report for the first time the IGF2 autocrine-mediated IGF1R activation as a nongenetic mechanism of osimertinib resistance in lung cancer at a clinically relevant level. IMPLICATIONS: Using comprehensive protein phosphorylation array and patient-derived lung cancer cells, we found that IGF2 autocrine-mediated IGF1R pathway activation is a clinically relevant and common mechanism of acquired resistance to osimertinib.

Mechanism of action of HTX-011: a novel, extended-release, dual-acting local anesthetic formulation for postoperative pain

BACKGROUND AND OBJECTIVES

Obtaining consistent efficacy beyond 12-24 hours with local anesthetics, including extended-release formulations, has been a challenging goal. Inflammation resulting from surgery lowers the pH of affected tissues, reducing neuronal penetration of local anesthetics. HTX-011, an investigational, nonopioid, extended-release dual-acting local anesthetic combining bupivacaine and low-dose meloxicam, was developed to reduce postsurgical pain through 72 hours using novel extended-release polymer technology. Preclinical studies and a phase II clinical trial were conducted to confirm the mechanism of action of HTX-011.

METHODS

In a validated postoperative pain pig model and a phase II bunionectomy trial, the analgesic effects of HTX-011, oral meloxicam (preclinical only), liposomal bupivacaine (preclinical only) and saline placebo were evaluated. The optimal meloxicam:bupivacaine ratio for HTX-011 and the effect of HTX-011 on incisional tissue pH were also evaluated preclinically.

RESULTS

Preclinical data demonstrate the ability of HTX-011 to address local tissue inflammation as demonstrated by a less acidic tissue pH, which was associated with potentiated and prolonged analgesic activity. In the phase II bunionectomy study, HTX-011 achieved superior and sustained pain relief through 72 hours after surgery compared with each component in the polymer.

CONCLUSIONS

Preclinical animal and clinical results confirm that the low-dose meloxicam in HTX-011 normalizes the local pH in the incision, resulting in superior and synergistic analgesic activity compared with extended-release bupivacaine. HTX-011 represents an extended-release local anesthetic with a dual-acting mechanism of action that may provide an important advancement in the treatment of postoperative pain.

TRIAL REGISTRATION NUMBER

NCT02762929.

Prediction of Cell Survival after Exposure to Mixed Radiation Fields with the Local Effect Model

Mixed radiation fields comprise the most common form of radiation exposure. Given their relevance in radiation protection, cancer radiotherapy and space research, accurate predictions of the corresponding radiation effects are essential. The local effect model (LEM) allows the prediction of cell survival after ion irradiation based on the knowledge of the cells' response to photon radiation. The assumption is made that the same spatial DNA double-strand break (DSB) distributions in the cell nucleus lead to the same effects, independent of the radiation quality that produced the DSBs. This makes the LEM an ideal tool for predictions of cell survival after exposure to any mixed radiation field. In this work, the LEM is applied to calculate cell survival for extreme mixed irradiation scenarios, i.e., high-linear energy transfer (LET) ion radiation combined with low-LET photon radiation, which can be understood as a consistency test for the high-LET model. Available experimental data covering several ion species and energies in combination with photon exposure are predicted with the LEM. Furthermore, the results are compared to the microdosimetric model by Zaider and Rossi and the lesion additivity model by Lam, which allow the prediction of cell survival after exposure to mixed radiation fields based on the knowledge of the survival curves of the two radiation components. Although the LEM uses only photon dose-response data as input, it is able to compete with the empirical radiobiological models that additionally require ion dose-response curves as input. Certain experimental scenarios are presented in which the specific consideration of spatial DSB distributions could be essential for an accurate prediction of the effect of mixed radiation fields.

Additive Dose Response Models: Defining Synergy

In synergy studies, one focuses on compound combinations that promise a synergistic or antagonistic effect. With the help of high-throughput techniques, a huge amount of compound combinations can be screened and filtered for suitable candidates for a more detailed analysis. Those promising candidates are chosen based on the deviance between a measured response and an expected non-interactive response. A non-interactive response is based on a principle of no interaction, such as Loewe Additivity or Bliss Independence. In a previous study, we introduced, an explicit formulation of the hitherto implicitly defined Loewe Additivity, the so-called Explicit Mean Equation. In the current study we show that this Explicit Mean Equation outperforms the original implicit formulation of Loewe Additivity and Bliss Independence when measuring synergy in terms of the deviance between measured and expected response, called the lack-of-fit. Further, we show that computing synergy as lack-of-fit outperforms a parametric approach. We show this on two datasets of compound combinations that are categorized into synergistic, non-interactive, and antagonistic.

Statistical determination of synergy based on Bliss definition of drugs independence

Although synergy is a pillar of modern pharmacology, toxicology, and medicine, there is no consensus on its definition despite its nearly one hundred-year history. Moreover, methods for statistical determination of synergy that account for variation of response to treatment are underdeveloped and if exist are reduced to the traditional t-test, but do not comply with the normal distribution assumption. We offer statistical models for estimation of synergy using an established definition of Bliss drugs’ independence. Although Bliss definition is well-known, it remains a theoretical concept and has never been applied for statistical determination of synergy with various forms of treatment outcome. We rigorously and consistently extend the Bliss definition to detect statistically significant synergy under various designs: (1) in vitro, when the outcome of a cell culture experiment with replicates is the proportion of surviving cells for a single dose or multiple doses, (2) dose-response methodology, (3) in vivo studies in organisms, when the outcome is a longitudinal measurement such as tumor volume, and (4) clinical studies, when the outcome of treatment is measured by survival. For each design, we developed a specific statistical model and demonstrated how to test for independence, synergy, and antagonism, and compute the associated p-value.

Isobologram Analysis: A Comprehensive Review of Methodology and Current Research

Drug combination is a common method for clinical disease treatment. Whether the combination of drugs is reasonable often affects the result of the disease treatment. Many methods have been used to evaluate interaction between drugs to date. Isobologram analysis has been mathematically proven and widely used to evaluate drug interactions. In this paper, the principle of isobologram analysis and its application in drug interaction evaluation are summarized. The applications of the similar cotoxicity coefficient and fractional inhibitory concentration index in the evaluation of drug interaction are also reviewed. This work is expected to evaluate the effect of formulations scientifically and provide scientific judgment standards for the development of formulations and clinical drug compatibility.

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.