Synergistic and antagonistic drug combinations depend on network topology

In vitro acaricidal activity of essential oils and their binary mixtures against ixodes scapularis (Acari: Ixodidae)

Ixodes scapularis ticks are vectors of infectious agents that cause illness in humans, including Lyme disease. Recent years have seen a surge in tick-borne diseases (TBD) resulting in a high demand for tick management products. Plants offer a valuable source of active compounds for the development of novel, eco-friendly tick control products, reducing potential risks to human and animal health. Essential oils (EOs) have emerged as potential acaricides and repellents against ticks providing an alternative to synthetic chemicals and aiding in the prevention of TBD by lowering the risk of tick bites. We investigated the acaricidal activity of EOs from lemongrass (Cymbopogon citratus), geranium (Pelargonium x asperum), savory thyme (Thymus saturejoides), and white thyme (Thymus zygis) on I. scapularis. The interactions (i.e., synergistic, antagonistic, or additive) of their binary mixtures were also evaluated. EO samples were analyzed via gas chromatography-mass spectrometry to determine their chemical composition. The adult immersion test was used to determine the lethal concentration (LC50) of each EO alone and in mixtures. Quantitative assessment of synergistic, additive, or antagonistic effect of the binary mixtures was performed by calculating the combination index. Strong acaricidal activity was recorded for savory thyme and white thyme EOs, with LC50 values of 28.0 and 11.0 μg/μL, respectively. The LC50 of lemongrass and geranium EOs were 49.0 and 39.7 μg/μL, respectively. Among the tested EOs, savory thyme and white thyme had a strong acaricidal effect on I. scapularis, which might be linked to the presence of carvacrol (26.05 % ± 0.38) and thymol (53.6 % ± 2.31), main components present in savory thyme and white thyme EOs, respectively. The tick killing efficacy of lemongrass and geranium EOs was lower when mixed than when used separately (LC50 of 65.3 µg/µL). The same happened with savory thyme and white thyme EOs, except at 9.75 µg/µL where they had a synergistic effect (LC50 of 58.3 µg/µL). Lemongrass and savory thyme EOs had a synergistic effect at low concentrations, and an antagonistic effect at higher concentrations (LC50 of 95.4 µg/µL). Lemongrass and white thyme EOs had a synergistic effect against ticks from 15 to 120 µg/µL (LC50 of 18.5 µg/µL) similar to white thyme EO. Geranium and savory thyme EOs had an antagonistic effect at all concentrations, with an LC50 of 66.8 µg/µL. Geranium and white thyme EOs also had an antagonistic effect, except at 12.7 µg/µL where they had a synergistic effect (LC50 of 66.8 µg/µL). The interaction observed when combining selected essential oils suggests promising potential for developing acaricidal formulations aimed at controlling ticks and curbing the transmission of tick-borne disease agents.

Caffeic acid, a dietary polyphenol, pre-sensitizes pancreatic ductal adenocarcinoma to chemotherapeutic drug

Resistance to chemotherapeutics is an eminent cause that leads to search for options that help in diminution of pancreatic ductal adenocarcinoma (PDAC) by overcoming resistance issues. Caffeic acid (CFA), a polyphenol occurring in many dietary foods, is known to show antidiabetic and anticancer properties potential. To unveil the effect of CFA on PDAC, we carried out this research in PDAC cells, following which we checked the combination effect of CFA and chemotherapeutics and pre-sensitization effects of CFA. Multitudinous web-based approaches were applied for identifying CFA targets in PDAC and then getting their interconnections. Subsequently, we manifested CFA effects by in-vitro analysis showing IC50 concentrations of 37.37 and 15.06 µM on Panc-1 and Mia-PaCa-2, respectively. The combination index of CFA with different drugs was explored which showed the antagonistic effects of combination treatment leading to further investigation of the pre-sensitizing effects. CFA pre-sensitization reduced IC50 concentration of doxorubicin in both PDAC cell lines which also triggered ROS generation determined by 2',7'-dichlorofluorescin diacetate assay. The differential gene expression analysis after CFA treatment showed discrete genes affected in both cells, i.e. N-Cad and Cas9 in Panc-1 and Pi3K/AkT/mTOR along with p53 in Mia-PaCa-2. Collectively, this study investigated the role of CFA as PDAC therapeutics and explored the mechanism in mitigating resistance of PDAC by sensitizing to chemotherapeutics.Communicated by Ramaswamy H. Sarma.

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.

Synergistic effect of a nonsteroidal anti-inflammatory drug in combination with topotecan on small cell lung cancer cells

BACKGROUND

The topoisomerase I inhibitor topotecan (TPT) is used in the treatment of recurrent small cell lung cancer (SCLC). However, the drug has a limited success rate and causes distress to patients due to its side effects, such as hematologic toxicities, including anemia and thrombocytopenia. Due to these pharmacokinetic limitations and undesirable side effects of chemotherapeutic drugs, the development of combination therapies has gained popularity in SCLC. Meclofenamic acid (MA), a nonsteroidal anti-inflammatory drug, has demonstrated anticancer effects on various types of cancers through different mechanisms. This study aims to investigate the potential synergistic effects of MA and TPT on the small cell lung cancer cell line DMS114.

METHODS AND RESULTS

To assess the cytotoxic and apoptotic effects of the combined treatment of MA and TPT, trypan blue exclusion assay, Annexin V, acridine orange/propidium iodide staining, western blot, and cell cycle analysis were conducted. The results demonstrated that the combination of MA and TPT elicited synergistic effects by enhancing toxicity in DMS114 cells (P < 0.01) without causing toxicity in healthy epithelial lung cells MRC5. The strongest synergistic effect was observed when the cells were treated with 60 µM MA and 10 nM TPT for 48 h (CI = 0,751; DRI = 10,871).

CONCLUSION

This study, for the first time, furnishes compelling evidence that MA and TPT synergistically reduce cellular proliferation and induce apoptosis in SCLC cells. Combinations of these drugs holds promise as a potential therapeutic strategy to improve efficacy and reduce the side effects associated with TPT.

The Effect of Berberine Follow by Blue Light Irradiation and Valproic Acid on the Growth Inhibition of MDA-MB-231 Breast Cancer Cells

Breast cancer is the second most common cancer after lung cancer in the world. Due to the anti-cancer properties of Berberine (Ber), in this study, the effect of combination therapy of Ber in the presence of blue LED irradiation and Valproic acid (Val) on the MDA-MB-231 breast cancer cell line was investigated. For this reason, after culturing the cells using different concentrations of Ber and Val, breast cancer cells were treated in both mono-treatment and combination therapy. In combination therapy, two modes were considered: (1) treatment with Val and then treatment with Ber in the dark or in presence of blue light irradiation (PDT)at a wavelength of 465 nm and energy of 30 J/cm2 for 15 min, and (2) treatment with Ber in the dark or PDT and then treated with Val. In all cases, cell viability, morphological changes, and colonization were assessed. Evaluation of apoptosis was performed by fluorescence microscope and flow cytometry. According to the results, combination therapy has a higher mortality rate compared to mono-treatment, and in combination therapy, treatment of cells first with Ber (10 µg/mL)-PDT and then treatment with Val (250 µg/mL) caused a significant reduction (P < 0/05) in the survival rate of cancer cells. According to the findings, it can be said that the use of Ber-PDT in combination with Val, in addition to reducing the dose of the drug, has shown a synergistic effect which can suggest the potential of this strategy as a new treatment.

Lipid metabolism as a target for cancer drug resistance: progress and prospects

Cancer is the world's leading cause of human death today, and the treatment process of cancer is highly complex. Chemotherapy and targeted therapy are commonly used in cancer treatment, and the emergence of drug resistance is a significant problem in cancer treatment. Therefore, the mechanism of drug resistance during cancer treatment has become a hot issue in current research. A series of studies have found that lipid metabolism is closely related to cancer drug resistance. This paper details the changes of lipid metabolism in drug resistance and how lipid metabolism affects drug resistance. More importantly, most studies have reported that combination therapy may lead to changes in lipid-related metabolic pathways, which may reverse the development of cancer drug resistance and enhance or rescue the sensitivity to therapeutic drugs. This paper summarizes the progress of drug design targeting lipid metabolism in improving drug resistance, and providing new ideas and strategies for future tumor treatment. Therefore, this paper reviews the issues of combining medications with lipid metabolism and drug resistance.

Review of Predicting Synergistic Drug Combinations

The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progression of drug resistance. This review presents the latest applications of methods for predicting the effects of drug combinations and the bioactivity databases commonly used in drug combination prediction. These studies have played a significant role in developing precision therapy. We first describe the concept of synergy. we study various publicly available databases for drug combination prediction tasks. Next, we introduce five algorithms applied to drug combinatorial prediction, which include traditional machine learning methods, deep learning methods, mathematical methods, systems biology methods and search algorithms. In the end, we sum up the difficulties encountered in prediction models.

Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling

Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.

De Novo Design of Molecules with Multiaction Potential from Differential Gene Expression using Variational Autoencoder

The modulating effect of chemical compounds and therapeutics on gene transcription is well-reported and has been intensively studied for both clinical and research purposes. Emerging research points toward the utility of drug-induced transcriptional alterations in de novo molecular design and highlights the idea of phenotype-matching an expression signature of interest to the structures being designed. In this work, we build an autoencoder-based generative model, BiCEV, around this concept. Our generative autoencoder has demonstrably generated a set of new molecules from gene expression input with notable validity (96%), uniqueness (98%), and internal diversity (0.77). Further, we attempted to validate BiCEV by testing the model on gene-knockdown profiles and combined signatures of synergistic drug pairs. From these investigations, we found the designed structures to be consistently high in collective quality. However, when their similarities to the supposed functional equivalents as determined by shared targets were considered, the findings were somewhat mixed. In spite of this, we believe the generative model merits further development in conjunction with in vitro corroboration to lend itself to being an assistive tool for drug discovery experts, particularly to support the initial stages of hit identification and lead optimization.

Extraction, phytochemical characterization and anti-cancer mechanism of Haritaki churna: An ayurvedic formulation

Haritaki churna (HC), a single herb ayurvedic formulations is known to be prescribed for various gastro-intestinal disorders in Ayurveda. Haritaki churna aqueous extract (HCAE) has anti-cancer activity against different types of cancer cells with an IC50 in the range of 50-97 μg/ml. Bioavailability of Haritaki Churna is very high in digestive track and treatment of colorectal cancer cells HCT-116, DLD1, HT-29 with HCAE reduces its cellular viability with anti-cancer IC50 70μg/ml. HCAE consumption is safe for human as it didn't affect the cellular viability of primary human PBMCs or non-cancerogenic HEK-293 cells. Haritaki churna was found to be stable in biological gastric fluids and bioactive agents are not losing their anti-cancer activity under such harsh conditions. The HPLC Chromatogram of HCAE is giving 13 major peaks and 11 minor peaks. Exploiting LC-MS, IR and NMR spectroscopic techniques, a total of 13 compounds were identified from HCAE namely Shikimic acid, Chebulic acid, gallic acid, 5-hydroxymethylfurfural, Protocatechuic acid, 4-O-galloyl-shikimic Acid, 5-O-galloyl-shikimic Acid, Methylgallate, corilagin, 1, 2, 6, Tri-O-galloyl β-D-glucose, chebulagic acid, chebulinic acid, and Ellagic acid. Reconstitution and subtraction of phytochemicals from the mixture indicate that Ellagic acid significantly contribute into anti-cancer effect of HCAE. Cancer cells treated with ellagic acid from HCAE were incapable of completing their cell-cycle and halted the cell-cycle at DNA synthesis S-Phase, as demonstrated by decreased cyclin A2 expression levels with increasing ellagic acid concentration. Halting of cells at S-phase causes induction of apoptosis in cancer cells. Cancer cells exhibiting DNA fragmentation, changes in expression of several apoptotic proteins such as Bcl2, cytochrome-c and formation of cleaved products of caspase 3 and PARP-1 suggests ellagic acid induces cell death via mitochondrial pathway of apoptosis.

Dual-drug loaded ultrasound-responsive nanodroplets for on-demand combination chemotherapy

Phase-changing nanodroplets are nanometric sized constructs that can be vaporized via external stimuli, such as focused ultrasound, to generate gaseous bubbles that are visible in ultrasound. Their activation can also be leveraged to release their payload, creating a method for ultrasound-modulated localized drug delivery. Here, we develop a perfluoropentane core nanodroplet that can simultaneously load paclitaxel and doxorubicin, and release them in response to an acoustic trigger. A double emulsion method is used to incorporate the two drugs with different physio-chemical properties, which allows for a combinatorial chemotherapy regimen to be used. Their loading, release, and biological effects on a triple negative breast cancer mouse model are investigated. We show that activation enhances the drug-delivery effect and delays the tumor growth rate in vivo. Overall, the phase-changing nanodroplets are a useful platform to allow on-demand delivery of combinations of drugs.

Neuropharmacological computational analysis of longitudinal electroencephalograms in clozapine-treated patients with schizophrenia using hierarchical dynamic causal modeling

The hierarchical characteristics of the brain are prominent in the pharmacological treatment of psychiatric diseases, primarily targeting cellular receptors that extend upward to intrinsic connectivity within a region, interregional connectivity, and, consequently, clinical observations such as an electroencephalogram (EEG). To understand the long-term effects of neuropharmacological intervention on neurobiological properties at different hierarchical levels, we explored long-term changes in neurobiological parameters of an N-methyl-D-aspartate canonical microcircuit model (CMM-NMDA) in the default mode network (DMN) and auditory hallucination network (AHN) using dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The neurobiological properties of the CMM-NMDA model associated with symptom improvement in schizophrenia were found across hierarchical levels, from a reduced membrane capacity of the deep pyramidal cell and intrinsic connectivity with the inhibitory population in DMN and intrinsic and extrinsic connectivity in AHN. The medication duration mainly affects the intrinsic connectivity and NMDA time constant in DMN. Virtual perturbation analysis specified the contribution of each parameter to the cross-spectral density (CSD) of the EEG, particularly intrinsic connectivity and membrane capacitances for CSD frequency shifts and progression. It further reveals that excitatory and inhibitory connectivity complements frequency-specific CSD changes, notably the alpha frequency band in DMN. Positive and negative synergistic interactions exist between neurobiological properties primarily within the same region in patients treated with clozapine. The current study shows how computational neuropharmacology helps explore the multiscale link between neurobiological properties and clinical observations and understand the long-term mechanism of neuropharmacological intervention reflected in clinical EEG.

Predicting anti-cancer drug combination responses with a temporal cell state network model

Cancer chemotherapy combines multiple drugs, but predicting the effects of drug combinations on cancer cell proliferation remains challenging, even for simple in vitro systems. We hypothesized that by combining knowledge of single drug dose responses and cell state transition network dynamics, we could predict how a population of cancer cells will respond to drug combinations. We tested this hypothesis here using three targeted inhibitors of different cell cycle states in two different cell lines in vitro. We formulated a Markov model to capture temporal cell state transitions between different cell cycle phases, with single drug data constraining how drug doses affect transition rates. This model was able to predict the landscape of all three different pairwise drug combinations across all dose ranges for both cell lines with no additional data. While further application to different cell lines, more drugs, additional cell state networks, and more complex co-culture or in vivo systems remain, this work demonstrates how currently available or attainable information could be sufficient for prediction of drug combination response for single cell lines in vitro.

Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance

BRAF is prototypical of oncogenes that can be targeted therapeutically and the treatment of BRAF^V600E melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring generates a drug adapted state thought to be involved in acquired resistance and disease recurrence. In this article, we study mechanisms of adaptive rewiring in BRAF^V600E melanoma cells using an energy-based implementation of ordinary differential equation (ODE) modeling in combination with proteomic, transcriptomic and imaging data. We develop a method for causal tracing of ODE models and identify two parallel MAPK reaction channels that are differentially sensitive to RAF and MEK inhibitors due to differences in protein oligomerization and drug binding. We describe how these channels, and timescale separation between immediate-early signaling and transcriptional feedback, create a state in which the RAS-regulated MAPK channel can be activated by growth factors under conditions in which the BRAF^V600E -driven channel is fully inhibited. Further development of the approaches in this article is expected to yield a unified model of adaptive drug resistance in melanoma.

A Synergistic pH-Responsive Serum Albumin-Based Drug Delivery System Loaded with Doxorubicin and Pentacyclic Triterpene Betulinic Acid for Potential Treatment of NSCLC

Nanosized drug delivery systems (DDS) have been studied as a novel strategy against cancer due to their potential to simultaneously decrease drug inactivation and systemic toxicity and increase passive and/or active drug accumulation within the tumor(s). Triterpenes are plant-derived compounds with interesting therapeutic properties. Betulinic acid (BeA) is a pentacyclic triterpene that has great cytotoxic activity against different cancer types. Herein, we developed a nanosized protein-based DDS of bovine serum albumin (BSA) as the drug carrier combining two compounds, doxorubicin (Dox) and the triterpene BeA, using an oil-water-like micro-emulsion method. We used spectrophotometric assays to determine protein and drug concentrations in the DDS. The biophysical properties of these DDS were characterized using dynamic light scattering (DLS) and circular dichroism (CD) spectroscopy, confirming nanoparticle (NP) formation and drug loading into the protein structure, respectively. The encapsulation efficiency was 77% for Dox and 18% for BeA. More than 50% of both drugs were released within 24 h at pH 6.8, while less drug was released at pH 7.4 in this period. Co-incubation viability assays of Dox and BeA alone for 24 h demonstrated synergistic cytotoxic activity in the low μM range against non-small-cell lung carcinoma (NSCLC) A549 cells. Viability assays of the BSA-(Dox+BeA) DDS demonstrated a higher synergistic cytotoxic activity than the two drugs with no carrier. Moreover, confocal microscopy analysis confirmed the cellular internalization of the DDS and the accumulation of the Dox in the nucleus. We determined the mechanism of action of the BSA-(Dox+BeA) DDS, confirming S-phase cell cycle arrest, DNA damage, caspase cascade activation, and downregulation of epidermal growth factor receptor (EGFR) expression. This DDS has the potential to synergistically maximize the therapeutic effect of Dox and diminish chemoresistance induced by EGFR expression using a natural triterpene against NSCLC.

Graph representation learning in biomedicine and healthcare

Networks-or graphs-are universal descriptors of systems of interacting elements. In biomedicine and healthcare, they can represent, for example, molecular interactions, signalling pathways, disease co-morbidities or healthcare systems. In this Perspective, we posit that representation learning can realize principles of network medicine, discuss successes and current limitations of the use of representation learning on graphs in biomedicine and healthcare, and outline algorithmic strategies that leverage the topology of graphs to embed them into compact vectorial spaces. We argue that graph representation learning will keep pushing forward machine learning for biomedicine and healthcare applications, including the identification of genetic variants underlying complex traits, the disentanglement of single-cell behaviours and their effects on health, the assistance of patients in diagnosis and treatment, and the development of safe and effective medicines.

Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism

Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs. SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. This is realized by considering the SDC graphs of different cell lines as a relational graph, and constructing a relational graph convolutional network (R-GCN) as the encoder to learn and fuse the deep representations of drugs for different cell lines. An attention mechanism is devised to integrate the drug features from different layers of the R-GCN according to their relative importance so that representation learning is further enhanced. The common patterns are exploited through partial parameter sharing in cell line-specific decoders, which not only reconstruct the known SDCs but also predict new ones for each cell line. Experiments on various datasets demonstrate that SDCNet is superior to state-of-the-art methods and is also robust when generalized to new cell lines that are different from the training ones. Finally, the case study again confirms the effectiveness of our method in predicting novel reliable cell line-specific SDCs.

The MEK1/2 Inhibitor ATR-002 (Zapnometinib) Synergistically Potentiates the Antiviral Effect of Direct-Acting Anti-SARS-CoV-2 Drugs

The coronavirus disease 2019 (COVID-19) represents a global public health burden. In addition to vaccination, safe and efficient antiviral treatment strategies to restrict the viral spread within the patient are urgently needed. An alternative approach to a single-drug therapy is the combinatory use of virus- and host-targeted antivirals, leading to a synergistic boost of the drugs’ impact. In this study, we investigated the property of the MEK1/2 inhibitor ATR-002’s (zapnometinib) ability to potentiate the effect of direct-acting antivirals (DAA) against SARS-CoV-2 on viral replication. Treatment combinations of ATR-002 with nucleoside inhibitors Molnupiravir and Remdesivir or 3C-like protease inhibitors Nirmatrelvir and Ritonavir, the ingredients of the drug Paxlovid, were examined in Calu-3 cells to evaluate the advantage of their combinatory use against a SARS-CoV-2 infection. Synergistic effects could be observed for all tested combinations of ATR-002 with DAAs, as calculated by four different reference models in a concentration range that was very well-tolerated by the cells. Our results show that ATR-002 has the potential to act synergistically in combination with direct-acting antivirals, allowing for a reduction in the effective concentrations of the individual drugs and reducing side effects.

Salvia miltiorrhiza in Breast Cancer Treatment: A Review of Its Phytochemistry, Derivatives, Nanoparticles, and Potential Mechanisms

Breast cancer is one of the most deadly malignancies in women worldwide. Salvia miltiorrhiza, a perennial plant that belongs to the genus Salvia, has long been used in the management of cardiovascular and cerebrovascular diseases. The main anti-breast cancer constituents in S. miltiorrhiza are liposoluble tanshinones including dihydrotanshinone I, tanshinone I, tanshinone IIA, and cryptotanshinone, and water-soluble phenolic acids represented by salvianolic acid A, salvianolic acid B, salvianolic acid C, and rosmarinic acid. These active components have potent efficacy on breast cancer in vitro and in vivo. The mechanisms mainly include induction of apoptosis, autophagy and cell cycle arrest, anti-metastasis, formation of cancer stem cells, and potentiation of antitumor immunity. This review summarized the main bioactive constituents of S. miltiorrhiza and their derivatives or nanoparticles that possess anti-breast cancer activity. Besides, the synergistic combination with other drugs and the underlying molecular mechanisms were also summarized to provide a reference for future research on S. miltiorrhiza for breast cancer treatment.

Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models

Mechanistic pharmacodynamic models that incorporate the binding kinetics of drug-target interactions have several advantages in understanding target engagement and the efficacy of a drug dose. However, guidelines on how to build and interpret mechanistic pharmacodynamic drug-target binding models considering both biological and computational factors are still missing in the literature. In this chapter, current approaches of building mechanistic PD models and their advantages are discussed. We also present a methodology on how to select a suitable model considering both biological and computational perspectives, as well as summarize the challenges of current mechanistic PD models.

Topological network measures for drug repositioning

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.

Dual Glycolate Oxidase/Lactate Dehydrogenase A Inhibitors for Primary Hyperoxaluria

Both glycolate oxidase (GO) and lactate dehydrogenase A (LDHA) influence the endogenous synthesis of oxalate and are clinically validated targets for treatment of primary hyperoxaluria (PH). We investigated whether dual inhibition of GO and LDHA may provide advantage over single agents in treating PH. Utilizing a structure-based drug design (SBDD) approach, we developed a series of novel, potent, dual GO/LDHA inhibitors. X-ray crystal structures of compound 15 bound to individual GO and LDHA proteins validated our SBDD strategy. Dual inhibitor 7 demonstrated an IC₅₀ of 88 nM for oxalate reduction in an Agxt-knockdown mouse hepatocyte assay. Limited by poor liver exposure, this series of dual inhibitors failed to demonstrate significant PD modulation in an in vivo mouse model. This work highlights the challenges in optimizing in vivo liver exposures for diacid containing compounds and limited benefit seen with dual GO/LDHA inhibitors over single agents alone in an in vitro setting.

Histopathological changes in Oreochromis mossambicus (Peters, 1852) ovaries after a chronic exposure to a mixture of the HIV drug nevirapine and the antibiotics sulfamethoxazole and trimethoprim

The burden of the human immunodeficiency virus and acquired immunodeficiency syndrome (HIV/AIDS) infection has transformed the African continent into a major consumer of antiretrovirals (ARVs) drugs. In addition to HIV burden, the African continent has also a high incidence of tuberculosis (TB) and has been experiencing recurring outbreaks of several other viral, bacterial, and parasitic epidemic diseases. The novel severe acute respiratory syndrome coronavirus 2 (SARS-COV-2 or Covid-19) pandemic outbreak is adding to the continent's infectious diseases burden as experts are predicting that it will be here for a long time. One of the consequences of these infectious diseases is that antiviral and antibiotic compounds have become some of the most consumed pharmaceuticals on the continent. Many of these drugs have been frequently detected in surface waters across Africa. There is limited information available on the adverse effects of the mixtures of different types of pharmaceuticals in African aquatic environments on fish reproduction. The present study investigated the effects of the ARV drug nevirapine (NVP - 1.48 and 3.74 μg/L) and its mixture with the antibiotic sulfamethoxazole (3.68 μg/L) and trimethoprim (0.87 μg/L) on O. mossambicus gonads using histopathological endpoints as biomarkers. The fish (n = 52) were exposed for 30 days in a static renewal system. Female O. mossambicus exposed to nevirapine (3.74 μg/L) and to NVP - antibiotic mixture recorded higher ovary indices. Statistically significant differences were found in female ovary indices between the fish exposed to NVP (3.74 μg/L) and the control fish (p = 0.002) as well as between the fish exposed to the NVP - antibiotic mixture and the control fish (p = 0.009). The main observed histopathological changes in the ovaries were increased vitellogenic oocyte atresia and vacuolation of the interstitial tissue in the fish exposed to NVP - antibiotic mixture. It is evident that the presence of NVP - antibiotics mixture in water triggered the observed histopathology in female fish ovaries. The detected abnormal high rate of atretic oocytes could result in impaired fish reproduction.

A Computational Approach for Pathway-Based Systemic Drug Influence

Drug repositioning is a well-known method used to reduce the time, cost, and development risks involved in bringing a new drug to the market. The rapid expansion of high-throughput datasets has enabled computational research that can suggest new potential uses for existing drugs. Some computational methods allow the prediction of potential drug targets of a given disease from a systematic network. Despite numerous efforts, the path of many drugs’ efficacy in the human body remains unclear. Therefore, the present study attempted to understand drug efficacy by systematically focusing on functional gene sets. The purpose of this study was to carry out modeling to identify systemic gene networks (called drug paths) in drug-specific pathways. In our results, we found five different paths for five different drugs.

Extending the lymphoblastoid cell line model for drug combination pharmacogenomics

Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.

Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics

Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors-let us call that the silver bullet approach to cancer therapeutics-to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns-we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases "shrapnel gunshots" may become more effective than "silver bullets". Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.

A Bilayer Vaginal Tablet for the Localized Delivery of Disulfiram and 5-Fluorouracil to the Cervix

This study was performed to develop an adjuvant therapy in the form of a self-administered vaginal tablet regimen for the localized delivery of chemotherapeutic drugs. This therapy will help to reduce relapse by eradicating cancerous cells in the margin of cervical tumors. The vaginal tablet is a very common formulation that is easy to manufacture, easy to place in the vagina, and has a low cost of manufacture, making them ideal for use in developing countries. A combination of disulfiram and 5-fluorouracil, which are both off-patent drugs and provide different modes of action, were evaluated. The tablets developed were evaluated for weight variation, thickness, hardness, friability, swelling index, differential scanning calorimetry (DSC), particle morphology, in vitro drug release, and cytotoxicity on Ca-Ski cells. Both layers were designed to release both drugs concurrently for a synergistic effect. The polymer–polymer interaction between the layers was able to reduce the loss of formulation due to chitosan. While the bilayer tablet had satisfactory performance in the physicochemical tests, in vitro cell culture with Ca-Ski also showed a synergistic effect using a combination of drugs at a low dose. However, the formulation only had 24-h dose release before degradation. Further drug combinations should be evaluated in subsequent studies.

Extreme Antagonism Arising from Gene-Environment Interactions

Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.

In Vitro Combinations of Baloxavir Acid and Other Inhibitors against Seasonal Influenza A Viruses

Two antiviral classes, the neuraminidase inhibitors (NAIs) and polymerase inhibitors (baloxavir marboxil and favipiravir) can be used to prevent and treat influenza infections during seasonal epidemics and pandemics. However, prolonged treatment may lead to the emergence of drug resistance. Therapeutic combinations constitute an alternative to prevent resistance and reduce antiviral doses. Therefore, we evaluated in vitro combinations of baloxavir acid (BXA) and other approved drugs against influenza A(H1N1)pdm09 and A(H3N2) subtypes. The determination of an effective concentration inhibiting virus cytopathic effects by 50% (EC50) for each drug and combination indexes (CIs) were based on cell viability. CompuSyn software was used to determine synergism, additivity or antagonism between drugs. Combinations of BXA and NAIs or favipiravir had synergistic effects on cell viability against the two influenza A subtypes. Those effects were confirmed using a physiological and predictive ex vivo reconstructed human airway epithelium model. On the other hand, the combination of BXA and ribavirin showed mixed results. Overall, BXA stands as a good candidate for combination with several existing drugs, notably oseltamivir and favipiravir, to improve in vitro antiviral activity. These results should be considered for further animal and clinical evaluations.

Predicting drug synergy for precision medicine using network biology and machine learning

Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. the synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.

Drug antagonism and single-agent dominance result from differences in death kinetics

Cancer treatment generally involves drugs used in combinations. Most previous work has focused on identifying and understanding synergistic drug-drug interactions; however, understanding antagonistic interactions remains an important and understudied issue. To enrich for antagonism and reveal common features of these combinations, we screened all pairwise combinations of drugs characterized as activators of regulated cell death. This network is strongly enriched for antagonism, particularly a form of antagonism that we call 'single-agent dominance'. Single-agent dominance refers to antagonisms in which a two-drug combination phenocopies one of the two agents. Dominance results from differences in cell death onset time, with dominant drugs acting earlier than their suppressed counterparts. We explored mechanisms by which parthanatotic agents dominate apoptotic agents, finding that dominance in this scenario is caused by mutually exclusive and conflicting use of Poly(ADP-ribose) polymerase 1 (PARP1). Taken together, our study reveals death kinetics as a predictive feature of antagonism, due to inhibitory crosstalk between cell death pathways.

Melanoma Cancer Immunotherapy Using PD-L1 siRNA and Imatinib Promotes Cancer-Immunity Cycle

PURPOSE

Cancer-Immunity Cycle is a cascade of anticancer immune responses in the body that continues and fights against the cancer expansion. The Cancer-Immunity Cycle is halted by tumor cell immunosuppression of host T cell through programmed cell death receptor 1 (PD-1) and programmed cell death ligand 1 (PD-L1) interactions that induce the functional suppression of tumor-reactive cytotoxic T cells and actively promotes the tumorigenesis via the mTOR signaling pathway.

METHODS

Here, we demonstrated that this Cycle could be enhanced by the synergistic knock down of PD-L1 through co-delivery of siRNA-PD-L1 (siPD-L1) and imatinib (IMT) in a liposomal nanoparticle.

RESULTS

The siPDIN effectively downregulated the protein expressions of PD-L1 and significantly knocked down the expression of p-S6k protein at in vitro and in vivo conditions which inhibited tumorigenic mTOR pathway. The combination-based siPDIN exhibited a significantly higher cytotoxic effect compared to that of individual anticancer agents. B16F10 cells treated with siPDIN exhibited a significantly higher cancer cell apoptosis (~60%) compared to cocktail combination of siRNA+IMT (~35%) analyzed by flow cytometer. Importantly, siPDIN significantly delayed the tumor growth with significantly lower tumor-specific growth rate than the animals treated with individual free IMT or siRNA. siPDIN produced a 3-fold higher IFN-γ compared to control in DLNs and 4-fold higher IFN-γ in spleens.

CONCLUSION

Overall, results revealed that the tumors treated with siPDIN restored the immunity of CTLs by potentially inhibiting the immune checkpoint interactions, suppressed the mTOR signaling pathway and exhibited an enhanced anticancer efficacy in melanoma.

A Cell-Based MAPK Reporter Assay Reveals Synergistic MAPK Pathway Activity Suppression by MAPK Inhibitor Combination in BRAF-Driven Pediatric Low-Grade Glioma Cells

Pilocytic astrocytomas as well as other pediatric low-grade gliomas (pLGG) exhibit genetic events leading to aberrant activation of the MAPK pathway. The most common alterations are KIAA1549:BRAF fusions and BRAF^V600E and NF1 mutations. Novel drugs targeting the MAPK pathway (MAPKi) are prime candidates for the treatment of these single-pathway diseases. We aimed to develop an assay suitable for preclinical testing of MAPKi in pLGGs with the goal to identify novel MAPK pathway-suppressing synergistic drug combinations. A reporter plasmid (pDIPZ) with a MAPK-responsive ELK-1-binding element driving the expression of destabilized firefly luciferase was generated and packaged using a lentiviral vector system. Pediatric glioma cell lines with a BRAF fusion (DKFZ-BT66) and a BRAF^V600E mutation (BT-40) background, respectively, were stably transfected. Modulation of the MAPK pathway activity by MAPKi was measured using the luciferase reporter and validated by detection of phosphorylated protein levels. A screening of a MAPKi library was performed, and synergy of selected combinations was calculated. Screening of a MAPKi library revealed MEK inhibitors as the class inhibiting the pathway with the lowest IC₅₀s, followed by ERK and next-generation RAF inhibitors. Combination treatments with different MAPKi classes showed synergistic effects in BRAF fusion as well as BRAF^V600E mutation backgrounds. Here, we report a novel reporter assay for medium- to high-throughput preclinical drug testing in pLGG cell lines. The assay confirmed MEK, ERK, and next-generation RAF inhibitors as potential treatment approaches for KIAA1549:BRAF and BRAF^V600E-mutated pLGGs. In addition, the assay revealed that combination treatments synergistically suppressed MAPK pathway activity.

Enhanced Prevention of Breast Tumor Metastasis by Nanoparticle-Delivered Vitamin E in Combination with Interferon-Gamma

Preventing cancer metastasis is one of the remaining challenges in cancer therapy. As an efficient natural product, alpha-tocopheryl succinate (α-TOS), the most effective form of vitamin E, holds great anticancer potential. To improve its efficacy and bioavailability, lipid-coated calcium carbonate/phosphate (LCCP) nanoparticles (NPs) with folic acid and PEG modification are synthesized for efficient delivery of α-TOS to 4T1 cancer cells. The optimized LCCP-FA NPs (NP-TOS15) show an α-TOS loading efficiency of around 60%, and enhanced uptake by 4T1 metastatic cancer cells. Consequently, NP-TOS15 significantly enhance the anticancer effect in combination with interferon-gamma (IFN-γ) in terms of apoptosis facilitation and migration inhibition. Importantly, NP-TOS15 upregulate the anticancer immunity via downregulating program death ligand 1 (PD-L1) expression that is initially induced by IFN-γ, and remarkably prevent the lung metastasis, particularly in combination with IFN-γ. Further investigation reveals that this combination therapy also modulates the cytotoxic lymphocyte infiltration into the tumor microenvironment for tumor elimination. Taken together, the NP delivery of α-TOS in combination with IFN-γ provides an applicable strategy for cancer therapy.

Bioinspired Nanocomposites: Applications in Disease Diagnosis and Treatment

Recent advancement in the field of synthesis and application of nanomaterials provided holistic approach for both diagnosis as well as treatment of diseases. Briefly, three-dimensional scaffold and geometry of bioinspired nanocarriers modulate bulk properties of loaded drug at molecular/ atomic structures in a way to conjointly modulate pathological as well as altered metabolic states of diseases, in very predictable and desired manners at a specific site of the target. While, from the pharmacotechnical point of views, the bioinspired nanotechnology processes carriers either favor to enhance the solubility of poorly aqueous soluble drugs or enable well-controlled sustained release profiles, to reduce the frequency of drug regimen. Consequently, from biopharmaceutical point of view, these composite materials, not only minimize first pass metabolism but also significantly enhance in-vivo biodistribution, permeability, bio-adhesion and diffusivity. In lieu of the above arguments, the nano-processed materials exhibit an important role for diagnosis and treatments. In the diagnostic center, recent emergences and advancement in the tools and techniques to diagnose the unrevealed diseases with the help of instruments such as, computed tomography, magnetic resonance imaging etc; heavily depend upon nanotechnology-based materials. In this paper, a brief introduction and recent application of different types of nanomaterials in the field of tissue engineering, cancer treatment, ocular therapy, orthopedics, and wound healing as well as drug delivery system are thoroughly discussed.

Leaf ethanolic extract of Etlingera hemesphaerica Blume alters mercuric chloride teratogenicity during the post-implantation period in Mus musculus

This study aimed to investigate the effectiveness of leaf ethanolic extract of Etlingera hemisphaerica (LE3H) against the teratogenic effects of mercuric chloride (HgCl₂) in mice (Mus musculus). Pregnant M. musculus were divided into four groups, each consisting of 10 dams, and received drink and food ad libitum. The first, second, and third, and fourth (control) groups were administered with LE3H, HgCl₂, HgCl₂ + LE3H, and double-distilled water alone, respectively. HgCl₂ (5 mg/kg bw) was administered by injection on gestation day (GD) 9, and LE3H (0.39 mg/g bw) was administered by gavage on GD 10. Treated and control animals were killed by cervical dislocation on GD 18, dissected, and the fetuses were collected for evaluation of maternal, embryonic, and fetal toxicity. Eight parameters were measured: (a) embryo resorption or resorbed embryo, (b) dead fetus, (c) living fetus, (d) morphologically normal living fetus, (e) malformed living fetus, (f) number of MLF, (g) length of MNLF, and (h) weight of MNLF. LE3H caused 4 (50.00%), whereas HgCl₂ resulted in 7 (87.50%) parameters that were significantly different from those of the control, indicating that the teratogenicity of HgCl₂ was significantly higher than that of LE3H. HgCl₂ + LE3H showed two effects of LE3H on the teratogenicity of HgCl₂: increased 2 (25.00%), and decreased 6 (75.00%). Thus, LE3H decreased the teratogenic effects of HgCl₂ in M. musculus.

BZD9L1 sirtuin inhibitor as a potential adjuvant for sensitization of colorectal cancer cells to 5-fluorouracil

Background:

This study aims to investigate the combination effect of a novel sirtuin inhibitor (BZD9L1) with 5-fluorouracil (5-FU) and to determine its molecular mechanism of action in colorectal cancer (CRC).

Methods:

BZD9L1 and 5-FU either as single treatment or in combination were tested against CRC cells to evaluate synergism in cytotoxicity, senescence and formation of micronucleus, cell cycle and apoptosis, as well as the regulation of related molecular players. The effects of combined treatments at different doses on stress and apoptosis, migration, invasion and cell death mechanism were evaluated through two-dimensional and three-dimensional cultures. In vivo studies include investigation on the combination effects of BZD9L1 and 5-FU on colorectal tumour xenograft growth and an evaluation of tumour proliferation and apoptosis using immunohistochemistry.

Results:

Combination treatments exerted synergistic reduction on cell viability on HCT 116 cells but not on HT-29 cells. Combined treatments reduced survival, induced cell cycle arrest, apoptosis, senescence and micronucleation in HCT 116 cells through modulation of multiple responsible molecular players and apoptosis pathways, with no effect in epithelial mesenchymal transition (EMT). Combination treatments regulated SIRT1 and SIRT2 protein expression levels differently and changed SIRT2 protein localization. Combined treatment reduced growth, migration, invasion and viability of HCT 116 spheroids through apoptosis, when compared with the single treatment. In addition, combined treatment was found to reduce tumour growth in vivo through reduction of tumour proliferation and necrosis compared with the vehicle control group. This highlights the potential therapeutic effects of BZD9L1 and 5-FU towards CRC.

Conclusion:

This study may pave the way for use of BZD9L1 as an adjuvant to 5-FU in improving the therapeutic efficacy for the treatment of colorectal cancer.

Pharmacological inhibition of the MEK5/ERK5 and PI3K/Akt signaling pathways synergistically reduces viability in triple-negative breast cancer

Triple-negative breast cancers (TNBCs) represent 15% to 20% of all breast cancers and are often associated with poor prognosis. The lack of targeted therapies for TNBCs contributes to higher mortality rates. Aberrations in the phosphoinositide-3-kinase (PI3K) and mitogen-activated protein kinase pathways have been linked to increased breast cancer proliferation and survival. It has been proposed that these survival characteristics are enhanced through compensatory signaling and crosstalk mechanisms. While the crosstalk between PI3K and extracellular signal-regulated kinase 1/2 (ERK1/2) pathways has been characterized in several systems, new evidence suggests that MEK5/ERK5 signaling is a key component in the proliferation and survival of several aggressive cancers. In this study, we examined the effects of dual inhibition of PI3K/protein kinase B (Akt) and MEK5/ERK5 in the MDA-MB-231, BT-549, and MDA-MB-468 TNBC cell lines. We used the Akt inhibitor ipatasertib, ERK5 inhibitors XMD8-92 and AX15836, and the novel MEK5 inhibitor SC-1-181 to investigate the effects of dual inhibition. Our results indicated that dual inhibition of PI3K/Akt and MEK5/ERK5 signaling was more effective at reducing the proliferation and survival of TNBCs than single inhibition of either pathway alone. In particular, a loss of Bad phosphorylation at two distinct sites was observed with dual inhibition. Furthermore, the inhibition of both pathways led to p21 restoration, decreased cell proliferation, and induced apoptosis. In addition, the dual inhibition strategy was determined to be synergistic in MDA-MB-231 and BT-549 cells and was relatively nontoxic in the nonneoplastic MCF-10 cell line. In summary, the results from this study provide a unique prospective into the utility of a novel dual inhibition strategy for targeting TNBCs.

PD-L1 Distribution and Perspective for Cancer Immunotherapy-Blockade, Knockdown, or Inhibition

Cancer immunotherapy involves blocking the interactions between the PD-1/PD-L1 immune checkpoints with antibodies. This has shown unprecedented positive outcomes in clinics. Particularly, the PD-L1 antibody therapy has shown the efficiency in blocking membrane PD-L1 and efficacy in treating some advanced carcinoma. However, this therapy has limited effects on many solid tumors, suspecting to be relevant to PD-L1 located in other cellular compartments, where they play additional roles and are associated with poor prognosis. In this review, we highlight the advances of 3 current strategies on PD-1/PD-L1 based immunotherapy, summarize cellular distribution of PD-L1, and review the versatile functions of intracellular PD-L1. The intracellular distribution and function of PD-L1 may indicate why not all antibody blockade is able to fully stop PD-L1 biological functions and effectively inhibit tumor growth. In this regard, gene silencing may have advantages over antibody blockade on suppression of PD-L1 sources and functions. Apart from cancer cells, PD-L1 silencing on host immune cells such as APC and DC can also enhance T cell immunity, leading to tumor clearance. Moreover, the molecular regulation of PD-L1 expression in cells is being elucidated, which helps identify potential therapeutic molecules to target PD-L1 production and improve clinical outcomes. Based on our understandings of PD-L1 distribution, regulation, and function, we prospect that the more effective PD-L1-based cancer immunotherapy will be combination therapies.

Combinations of Plant Essential Oil Based Terpene Compounds as Larvicidal and Adulticidal Agent against Aedes aegypti (Diptera: Culicidae)

Insecticidal plant-based compound(s)in combinations may show synergistic or antagonistic interactions against insect pest. Considering the rapid spread of the Aedes borne diseases and increasing resistance among Aedes population against conventional insecticides, twenty-eight combinations of plant essential oil-based terpene compounds were prepared and tested against larval and adult stages ofAedes aegypti. Initially five plant essential oils (EOs) were assessed for their larvicidal and adulticidal efficacy and two of their major compounds from each EO were identified from GC-MS results. Identified major compounds namely Diallyldisulfide, Diallyltrisulfide, Carvone, Limonene, Eugenol, Methyl Eugenol, Eucalyptol, Eudesmol and α-pinene were purchased and tested individually against A. aegypti. Binary combinations of these compounds were then prepared using sub-lethal doses, tested and their synergistic and antagonistic effects were determined. The best larvicidal compositions were obtained while Limonene was mixed with Diallyldisulfide and the best adulticidal composition was obtained while Carvone was mixed with Limonene. Commercially used synthetic larvicide "Temephos" and adulticide "Malathion" were tested individually and in binary combinations with the terpene compounds. The results revealed that the combination of Temephos and Diallyldisulfide and combination of Malathion and Eudesmol were the most effective combination. These effective combinations bear potential prospect to be used against Aedes aegypti.

Antifungal activity of two oxadiazole compounds for the paracoccidioidomycosis treatment

Paracoccidioidomycosis (PCM) is a neglected disease present in Latin America with difficulty in treatment and occurrence of serious sequelae. Thus, the development of alternative therapies is imperative. In the current work, two oxadiazole compounds (LMM5 and LMM11) presented fungicidal activity against Paracoccidioides spp. The minimum inhibitory and fungicidal concentration values ranged from 1 to 32 μg/mL, and a synergic effect was observed for both compounds when combined with Amphotericin B. LMM5 and LMM11 were able to reduce CFU counts (≥2 log10) on the 5th and 7th days of time-kill curve, respectively. The fungicide effect was confirmed by fluorescence microscopy (FUN-1/FUN-2). The hippocratic screening and biochemical analysis were performed in Balb/c male mice that received a high dose of each compound, and the compounds showed no in vivo toxicity. The treatment of experimental PCM with the new oxadiazoles led to significant reduction in CFU (≥1 log10). Histopathological analysis of the groups treated exhibited control of inflammation, as well as preserved lung areas. These findings suggest that LMM5 and LMM11 are promising hits structures, opening the door for implementing new PCM therapies.

Natural Product Target Network Reveals Potential for Cancer Combination Therapies

A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.

Drug Combinations: Mathematical Modeling and Networking Methods

Treatments consisting of mixtures of pharmacological agents have been shown to have superior effects to treatments involving single compounds. Given the vast amount of possible combinations involving multiple drugs and the restrictions in time and resources required to test all such combinations in vitro, mathematical methods are essential to model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of the combination. In this review, we investigate various mathematical methods that model combination therapies. This survey includes the methods that focus on predicting the outcome of drug combinations with respect to synergism and antagonism, as well as the methods that explore the dynamics of combination therapy and its role in combating drug resistance. This comprehensive investigation of the mathematical methods includes models that employ pharmacodynamics equations, those that rely on signaling and how the underlying chemical networks are affected by the topological structure of the target proteins, and models that are based on stochastic models for evolutionary dynamics. Additionally, this article reviews computational methods including mathematical algorithms, machine learning, and search algorithms that can identify promising combinations of drug compounds. A description of existing data and software resources is provided that can support investigations in drug combination therapies. Finally, the article concludes with a summary of future directions for investigation by the research community.

Synergistic Effects of Nobiletin and Sorafenib Combination on Metastatic Prostate Cancer Cells

Objective: Herein we, for the first time, investigated a potential synergistic effect of nobiletin (NOB) and sorafenib (SOR) on PC-3 prostate cancer and HUVEC control cell lines. Methods: In order to determine the cytotoxic and apoptotic effects of the combination of NOB and SOR, WST-1, Annexin V, and cell cycle analysis were performed. The potential molecular mechanism of apoptotic cell death was assessed by Bax, Bcl-2, CCDN1, Rb1, and CDKN1A gene expression and acridine orange (AO) and DAPI staining. Results: Our results indicated that NOB and SOR combination had a significant inhibitory effect on the viability of PC-3 cells with less toxicity on HUVEC cells than SOR alone (P < 0.01). NOB and SOR combination significantly caused much more apoptotic cell death and cell cycle arrest at G0/G1 phase by up-regulation of Bax, Rb1, and CDKN1A levels in PC-3 cells (P < 0.01). Therefore, strong synergistic effects between NOB and SOR were analyzed (CI < 1). Conclusion: NOB and SOR combination was more effective than SOR and NOB alone and reduced the exposure time for SOR and NOB in PC-3 cells. Combination strategy is a therapeutic potential to improve efficacy and reduce side-effect of SOR for the treatment of metastatic prostate cancer cells.

Viability Screen of LOPAC1280 Reveals Tyrosine Kinase Inhibitor Tyrphostin A9 as a Novel Partner Drug for Artesunate Combinations To Target the Plasmodium falciparum Ring Stage

The emergence of artemisinin-resistant Plasmodium falciparum poses a major threat to current frontline artemisinin combination therapies. Artemisinin resistance is widely associated with mutations in the P. falciparum Kelch13 (PfKelch13) propeller region, leading to delayed parasite clearance and increased survival of early-ring-stage parasites. There is therefore a need to discover novel drugs that are effective against artemisinin-resistant P. falciparum In view of this, our study aimed to identify compounds from the Library of Pharmacologically Active Compounds¹²⁸⁰ (LOPAC¹²⁸⁰) that could increase the efficacy of artesunate and be used as a potential partner drug for treatment against artemisinin-resistant falciparum malaria. By using a modified ring-stage survival assay, we performed a high-throughput screening of the activities of the 1,280 compounds from the LOPAC library in combination with artesunate against the P. falciparum IPC 5202 field isolate harboring the R539T mutation in the PfKelch13 propeller region. The potencies of the hits against both the IPC 5202 and CamWT_C580Y field isolates were determined through dose-dependent isobologram analyses; CamWT_C580Y has the more prevalent C580Y mutation characteristic of strains with artemisinin resistance. We identified tyrphostin A9 to have synergistic and additive activity against both parasite strains when dosed in combination with artesunate. These findings provide promising novel artesunate combinations that can target the P. falciparum artemisinin-resistant ring stage and insights that may aid in obtaining a better understanding of the mechanism involved in artemisinin resistance.