Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks

Background Drug combination, offering an insight into the increased therapeutic efficacy and reduced toxicity, plays an essential role in the therapy of many complex diseases. Although significant efforts have been devoted to the identification of drugs, the identification of drug combination is still a challenge. The current algorithms assume that the independence of feature selection and drug prediction procedures, which may result in an undesirable performance. Results To address this issue, we develop a novel Semi-supervised Heterogeneous Network Embedding algorithm (called SeHNE) to predict the combination patterns of drugs by exploiting the graph embedding. Specifically, the ATC similarity of drugs, drug–target, and protein–protein interaction networks are integrated to construct the heterogeneous networks. Then, SeHNE jointly learns drug features by exploiting the topological structure of heterogeneous networks and predicting drug combination. One distinct advantage of SeHNE is that features of drugs are extracted under the guidance of classification, which improves the quality of features, thereby enhancing the performance of prediction of drugs. Experimental results demonstrate that the proposed algorithm is more accurate than state-of-the-art methods on various data, implying that the joint learning is promising for the identification of drug combination. Conclusions The proposed model and algorithm provide an effective strategy for the prediction of combinatorial patterns of drugs, implying that the graph-based drug prediction is promising for the discovery of drugs.

ICAM-1 Targeted Drug Combination Nanoparticles Enhanced Gemcitabine-Paclitaxel Exposure and Breast Cancer Suppression in Mouse Models

Despite the availability of molecularly targeted treatments such as antibodies and small molecules for human epidermal growth factor receptor 2 (HER2), hormone receptor (HR), and programmed death-ligand 1 (PD-L1), limited treatment options are available for advanced metastatic breast cancer (MBC), which constitutes ~90% mortality. Many of these monotherapies often lead to drug resistance. Novel MBC-targeted drug-combination therapeutic approaches that may reduce resistance are urgently needed. We investigated intercellular adhesion molecule-1 (ICAM-1), which is abundant in MBC, as a potential target to co-localize two current drug combinations, gemcitabine (G) and paclitaxel (T), assembled in a novel drug-combination nanoparticle (GT DcNP) form. With an ICAM-1-binding peptide (referred to as LFA1-P) coated on GT DcNPs, we evaluated the role of the LFA1-P density in breast cancer cell localization in vitro and in vivo. We found that 1–2% LFA1-P peptide incorporated on GT DcNPs provided optimal cancer cell binding in vitro with ~4× enhancement compared to non-peptide GT DcNPs. The in vivo probing of GT DcNPs labeled with a near-infrared marker, indocyanine green, in mice by bio-imaging and G and T analyses indicated LFA1-P enhanced drug and GT DcNP localization in breast cancer cells. The target/healthy tissue (lung/gastrointestinal (GI)) ratio of particles increased by ~60× compared to the non-ligand control. Collectively, these data indicated that LFA1 on GT DcNPs may provide ICAM-1-targeted G and T drug combination delivery to advancing MBC cells found in lung tissues. As ICAM-1 is generally expressed even in breast cancers that are triple-negative phenotypes, which are unresponsive to inhibitors of nuclear receptors or HER2/estrogen receptor (ER) agents, ICAM-1-targeted LFA1-P-coated GT DcNPs should be considered for clinical development to improve therapeutic outcomes of MBCs.

Synergism in Antiplasmodial Activities of Artemether and Lumefantrine in Combination with Securidaca longipedunculata Fresen (Polygalaceae)

Malaria is the most lethal parasitic disease in the world. The frequent emergence of resistance by malaria parasites to any drug is the hallmark of sustained malaria burden. Since the deployment of artemisinin-based combination therapies (ACTs) it is clear that for a sustained fight against malaria, drug combination is one of the strategies toward malaria elimination. In Sub-Saharan Africa where malaria prevalence is the highest, the identification of plants with a novel mechanism of action that is devoid of cross-resistance is a feasible strategy in drug combination therapy. Thus, artemether and lumefantrine were separately combined and tested with extracts of Securidaca longipedunculata, a plant widely used to treat malaria, at fixed extract–drug ratios of 4:1, 3:1, 1:1, 1:2, 1:3, and 1:4. These combinations were tested for antiplasmodial activity against three strains of Plasmodium falciparum (W2, D6, and DD2), and seven field isolates that were characterized for molecular and ex vivo drug resistance profiles. The mean sum of fifty-percent fractional inhibition concentration (FIC50) of each combination and singly was determined. Synergism was observed across all fixed doses when roots extracts were combined with artemether against D6 strain (FIC50 0.403 ± 0.068) and stems extract combined with lumefantrine against DD2 strain (FIC50 0.376 ± 0.096) as well as field isolates (FIC50 0.656 ± 0.067). Similarly, synergism was observed in all ratios when leaves extract were combined with lumefantrine against W2 strain (FIC50 0.456 ± 0.165). Synergism was observed in most combinations indicating the potential use of S. longipedunculata in combination with artemether and lumefantrine in combating resistance.

Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks

Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug–drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications. Recently, numerous text mining– and machine learning–based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug–drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types.

Cardiotoxicity among Patients Using Pertuzumab, Trastuzumab and Taxane with HER2-Positive Early-Stage Breast Cancer: A Single-Centre Experience

Aim: In this study, we aimed to investigate the incidence rate, risk factors, and mortality rates in patients with early-stage breast cancer using anti-HER2 (Human epidermal growth factor receptor-2) treatment. Patients and Methods: A total of 106 patients diagnosed with human epidermal growth factor 2 (HER2)-positive early-stage breast cancer and receiving anti-HER2 treatment at King Abdulaziz Medical City (KAMC) from 2015 to 2019 were included in the analysis to assess the incidence of cardiotoxicity was collected as a retrospective study. Univariate and multivariate analyses as well as multiple exact logistic regression analysis were conducted to understand the relationships between the left ventricular ejection fraction (LVEF) and treatment combinations and comorbidities Results: The LVEF measurements using an echocardiography method at the baseline (before any treatment) and during the anti-HER2 therapy were assessed. The results suggest that the higher the drug combination, the higher the odds ratio for the declined ejection fraction (EF) patient group. Further, patients treated with the pertuzumab and trastuzumab combination were four times more likely to have a decline in their EF than those who did not use the pertuzumab and trastuzumab drug combination (OR 4.28, 95% CI [1.68–10.91]). Conclusion: This study demonstrated that the drug combination considered here is associated with reduced LVEF and, similarly, comorbidities were also related to EF. However, a larger study in a global patient population will confirm the present observations.

Potential Combination Drug Therapy to Prevent Redox Stress and Mitophagy Dysregulation in Retinal Müller Cells under High Glucose Conditions: Implications for Diabetic Retinopathy

Chronic hyperglycemia-induced thioredoxin-interacting protein (TXNIP) expression, associated oxidative/nitrosative stress (ROS/RNS), and mitochondrial dysfunction play critical roles in the etiology of diabetic retinopathy (DR). However, there is no effective drug treatment to prevent or slow down the progression of DR. The purpose of this study is to examine if a combination drug treatment targeting TXNIP and the mitochondria-lysosome pathway prevents high glucose-induced mitochondrial stress and mitophagic flux in retinal Müller glial cells in culture, relevant to DR. We show that diabetes induces TXNIP expression, redox stress, and Müller glia activation (gliosis) in rat retinas when compared to non-diabetic rat retinas. Furthermore, high glucose (HG, 25 mM versus low glucose, LG 5.5 mM) also induces TXNIP expression and mitochondrial stress in a rat retinal Müller cell line, rMC1, in in vitro cultures. Additionally, we develop a mitochondria-targeted mCherry and EGFP probe tagged with two tandem COX8a mitochondrial target sequences (adenovirus-CMV-2×mt8a-CG) to examine mitophagic flux in rMC1. A triple drug combination treatment was applied using TXNIP-IN1 (which inhibits TXNIP interaction with thioredoxin), Mito-Tempo (mitochondrial anti-oxidant), and ML-SA1 (lysosome targeted activator of transient calcium channel MCOLN1/TRPML1 and of transcription factor TFEB) to study the mitochondrial–lysosomal axis dysregulation. We found that HG induces TXNIP expression, redox stress, and mitophagic flux in rMC1 versus LG. Treatment with the triple drug combination prevents mitophagic flux and restores transcription factor TFEB and PGC1α nuclear localization under HG, which is critical for lysosome biosynthesis and mitogenesis, respectively. Our results demonstrate that 2×mt8a-CG is a suitable probe for monitoring mitophagic flux, both in live and fixed cells in in vitro experiments, which may also be applicable to in vivo animal studies, and that the triple drug combination treatment has the potential for preventing retinal injury and disease progression in diabetes.

Mutational switch-backs can accelerate evolution of Francisella to a combination of ciprofloxacin and doxycycline

Combination antimicrobial therapy has been considered a promising strategy to combat the evolution of antimicrobial resistance. Francisella tularensis is the causative agent of tularemia and in addition to being found in the nature, is recognized as a threat agent that requires vigilance. We investigated the evolutionary outcome of adapting the Live Vaccine Strain (LVS) of Francisella to two non-interacting drugs, ciprofloxacin and doxycycline, individually, sequentially, and in combination. Despite their individual efficacies and independence of mechanisms, evolution to the combination appeared to progress faster than evolution to the two drugs sequentially. We conducted a longitudinal mutational analysis of the populations evolving to the drug combination, genetically reconstructed the identified evolutionary pathway, and carried out biochemical validation. We discovered that, after the appearance of an initial weak generalist mutation (FupA/B), each successive mutation alternated between adaptation to one drug or the other. In combination, these mutations allowed the population to more efficiently ascend the fitness peak through a series of evolutionary switch-backs. Clonal interference, weak pleiotropy, and positive epistasis also contributed to combinatorial evolution. This finding suggests that, under some selection conditions, the use of non-interacting drug pairs as a treatment strategy may result in a more rapid ascent to multi-drug resistance and serves as a cautionary tale.Author summaryThe antimicrobial resistance crisis requires the use of novel treatment strategies to prevent or delay the emergence of resistance. Combinations of drugs offer one strategy to delay resistance, but the efficacy of such drug combinations depends on the evolutionary response of the organism. Using experimental evolution, we show that under some conditions, a potential drug combination does not delay the onset of resistance in bacteria responsible for causing tularemia, Francisella. In fact, they evolve resistance to the combination faster than when the two drugs are applied sequentially. This result is surprising and concerning: using this drug combination in a hospital setting could lead to simultaneous emergence of resistance to two antibiotics. Employing whole genome sequencing, we identified the molecular mechanism leading to evolution of resistance to the combination. The mechanism is similar to the switch-back route used by hikers while scaling steep mountains i.e., instead of simultaneously acquiring mutations conferring resistance to both drugs, the bacteria acquire mutations to each drug in alternating manner. Rather than scaling the steep mountain directly, the bacteria ascend the mountain by a series of evolutionary switch-backs to gain elevation and in doing so, they get to the top more efficiently.

Dose Optimization of Combined Linezolid and Fosfomycin against Enterococcus by Using an In Vitro Pharmacokinetic/Pharmacodynamic Model

In this study, we found that linezolid combined with fosfomycin could kill Enterococcus in vitro and that the administered dose was significantly lower after the combination treatment, which could reduce adverse effects and the development of drug resistance. The potential mechanism of the two-drug combination against Enterococcus was revealed from a quantitative perspective, which is an important step toward dose optimization in simulated humans.