Mechanisms of drug combinations: interaction and network perspectives. Nat Rev Drug Discov. 2009;8:111-128. [PMID: 19180105 DOI: 10.1038/nrd2683]

A Molecular Fragment Representation Learning Framework for Drug-Drug Interaction Prediction

The concurrent use of multiple drugs may result in drug-drug interactions, increasing the risk of adverse reactions. Hence, it is particularly crucial to propose computational methods for precisely identifying unknown drug-drug interactions, which is of great significance for drug development and health. However, most recent studies have limited the drug-drug interaction prediction task to identifying interactions between substructures, overlooking molecular hierarchical information. Moreover, the extracted substructures in these methods are always restricted to have the same number of atoms as contained in the molecular graph, which does not align with real-world facts. In this study, a molecular fragment representation learning framework for drug-drug interaction prediction is introduced. Initially, a fragment extraction module is designed to acquire a series of molecular fragments. Subsequently, to capture more comprehensive features, molecular hierarchical information is effectively integrated, enabling drug-drug interaction prediction by identifying pairwise interactions between molecular fragments of each drug. Comprehensive evaluations demonstrate that the proposed method achieved state-of-the-art performance in both DrugBank and Twosides datasets, particularly achieving an improved accuracy of over 20% for unseen drugs in both two datasets. Furthermore, case studies and visual analysis confirm that the proposed method can accurately identify crucial substructures influencing the interactions, which are basically consistent with functional group structures in reality. In conclusion, this method not only enhances the performance of drug-drug interaction prediction but also offers high interpretability. Source code is freely available at https://github.com/kennysyp/MFR-DDI .

Investigation of the synergistic effect mechanism underlying sequential use of palbociclib and cisplatin through integral proteomic and glycoproteomic analysis

Chemoresistance largely hampers the clinical use of chemodrugs for cancer patients, combination or sequential drug treatment regimens have been designed to minimize chemotoxicity and resensitize chemoresistance. In this work, the cytotoxic effect of cisplatin was found to be enhanced by palbociclib pretreatment in HeLa cells. With the integration of liquid chromatography-mass spectrometry-based proteomic and N-glycoproteomic workflow, we found that palbociclib alone mainly enhanced the N-glycosylation alterations in HeLa cells, while cisplatin majorly increased the different expression proteins related to apoptosis pathways. As a result, the sequential use of two drugs induced a higher expression level of apoptosis proteins BAX and BAK. Those altered N-glycoproteins induced by palbociclib were implicated in pathways that were closely associated with cell membrane modification and drug sensitivity. Specifically, the top four frequently glycosylated proteins FOLR1, L1CAM, CD63, and LAMP1 were all associated with drug resistance or drug sensitivity. It is suspected that palbociclib-induced N-glycosylation on the membrane protein allowed the HeLa cell to become more vulnerable to cisplatin treatment. Our study provides new insights into the mechanisms underlying the sequential use of target drugs and chemotherapy drugs, meanwhile suggesting a high-efficiency approach that involves proteomic and N-glycoproteomic to facilitate drug discovery.

A weighted Bayesian integration method for predicting drug combination using heterogeneous data

BACKGROUND

In the management of complex diseases, the strategic adoption of combination therapy has gained considerable prominence. Combination therapy not only holds the potential to enhance treatment efficacy but also to alleviate the side effects caused by excessive use of a single drug. Presently, the exploration of combination therapy encounters significant challenges due to the vast spectrum of potential drug combinations, necessitating the development of efficient screening strategies.

METHODS

In this study, we propose a prediction scoring method that integrates heterogeneous data using a weighted Bayesian method for drug combination prediction. Heterogeneous data refers to different types of data related to drugs, such as chemical, pharmacological, and target profiles. By constructing a multiplex drug similarity network, we formulate new features for drug pairs and propose a novel Bayesian-based integration scheme with the introduction of weights to integrate information from various sources. This method yields support strength scores for drug combinations to assess their potential effectiveness.

RESULTS

Upon comprehensive comparison with other methods, our method shows superior performance across multiple metrics, including the Area Under the Receiver Operating Characteristic Curve, accuracy, precision, and recall. Furthermore, literature validation shows that many top-ranked drug combinations based on the support strength score, such as goserelin and letrozole, have been experimentally or clinically validated for their effectiveness.

CONCLUSIONS

Our findings have significant clinical and practical implications. This new method enhances the performance of drug combination predictions, enabling effective pre-screening for trials and, thereby, benefiting clinical treatments. Future research should focus on developing new methods for application in various scenarios and for integrating diverse data sources.

Systematic analysis of traditional Chinese medicine prescriptions provides new insights into drug combination therapy for pox

ETHNOPHARMACOLOGICAL RELEVANCE

The decline in cross-protection provided by the smallpox vaccine increases the risk of infection from other poxviruses. While drug combinations are a promising management, they remain underdeveloped for poxviruses. Prior to the development of the smallpox vaccine, China had long relied on herbal medicine to combat pox and accumulated a wealth of knowledge regarding different herb combinations and symptoms related to pox. The information was documented in the form of prescriptions.

AIM OF THE STUDY

The extensive data of prescriptions offer the potential for uncovering commonalities underlying these prescriptions, thereby providing valuable insights into the development of drug combinations against pox.

MATERIALS AND METHODS

The 2344 prescriptions were collected from the LTM-TCM database and 12 traditional Chinese medicine books. Firstly, the relative frequency of citation was utilized to identify the most used herbs among these prescriptions. TCMSP and LTM-TCM databases were employed to gather information about active compounds and their targets. GeneCards and DisGeNET databases were utilized to determine the associated targets for smallpox, cowpox, chickenpox, and mpox. Subsequently, network pharmacology analysis was conducted to investigate potential pathway information related to the most used herbs. A comparison of active compounds from these herbs resulted in the identification of 29 high-frequency compounds. The functions of these compounds were elucidated through gene overlap analysis, docking, and literature review. Finally, we summarized pox-related symptoms and used fidelity levels to distinguish specific herbs for corresponding symptoms.

RESULTS

Based on 2344 traditional pox-related prescriptions, we identified 19 most used herbs and 64 associated bio-functional modules for poxvirus treatment, with the most significant one being immunoregulation primarily involving CD4+ regulation. We also identified 29 leads that possess anti-inflammatory, antimicrobial, and antiviral properties. These herbs and leads hold the potential for pox treatment. Additionally, docking analysis suggested that these leads could inhibit poxvirus DNA synthesis, RNA capping machinery processes, and mature poxvirus particle formation, as well as immunosuppressors. The clinical features of mpox in 2022 were found to align well with our description of symptoms related to the pox.

CONCLUSION

Through the analysis of 2344 prescriptions for pox treatment, we obtained a comprehensive library of the most used herbs and high-frequency compounds, along with their potential functional spectrum. These libraries served as raw resources for drug combination development, while the identified symptom patterns and specific herbs greatly enhanced our insight into diverse treatments for pox patients.

Can acclimation of freshwater rotifers to silver nanoparticles or 5-fluorouracil influence their multi- and transgenerational effects?

Emerging chemical contaminants (ECCs) are among the major environmental threats in present century. A variety of ECCs is released into aquatic environments with little knowledge about their long-term impacts to organisms. We examined the role of acclimation of the freshwater rotifer Brachionus calyciflorus to silver nanoparticles (Ag-NPs) and 5-fluorouracil (5-FU) for determining their ability to deal with these ECCs individually and in mixtures along multiple generations. Additionally, transgenerational effects were also assessed during the recovery phase. Rotifers acclimated at EC10 of Ag-NPs along generations showed a higher ability to deal with higher concentrations of these nanoparticles or 5-FU along generations. Rotifers acclimated to EC10 of 5-FU showed varied responses, as their population growth rates were affected at the initial generations once exposed to higher concentration (EC50) of the same or a new contaminant; however, the rotifers acquired resistance in later generations. The exposure of generational Ag-NP-acclimated rotifers to the mixture of Ag-NPs and 5-FU at EC50 led to a shift from no effects to negative effects along successive generations, suggesting a decrease in resistance, which remained even in the post-exposure recovery phase. Similar transgenerational adverse effects were also observed for the generational Ag-NP-acclimated rotifers released from 5-FU. Rotifers acclimated to 5-FU showed a decrease in population growth rate at the first generation of recovery phase, possibly shifting their optimal environmental conditions when released from contaminants. Overall, our results suggest that rotifers had a high level of plasticity to ECC exposure in freshwaters; however, acclimation can be generic or contaminant dependent.

Anti-inflammatory effects of polydeoxyribonucleotide and adipose tissue-derived mesenchymal stem cells in a canine cell model of osteoarthritis

IMPORTANCE

A relatively new therapeutic agent for osteoarthritis (OA), polydeoxyribonucleotide (PDRN), shows potential in treating human OA due to its regenerative and anti-inflammatory effects. However, studies on PDRN for canine OA are limited, and no study has investigated their use with mesenchymal stem cells (MSCs) conventionally used for OA treatment.

OBJECTIVE

This study aimed to evaluate the potential of PDRN and explore its combined effect with adipose tissue-derived MSCs (AdMSCs) in treating canine OA.

METHODS

To study the impact of PDRN, canine chondrocytes, synoviocytes, and AdMSCs were exposed to various PDRN concentrations, and viability was assessed using cell counting kit-8. The OA model was created by treating chondrocytes and synoviocytes with lipopolysaccharide, followed by treatment under three different conditions: PDRN alone, AdMSCs alone, and a combination of PDRN and AdMSCs. Using real-time quantitative polymerase chain reaction, the anti-inflammatory effects and mechanisms were investigated by quantitatively assessing pro-inflammatory cytokines, collagen degradation markers, adenosine A2a receptor (ADORA2A), and nuclear factor-kappa B.

RESULTS

PDRN alone and combined with AdMSCs significantly reduced the expression of pro-inflammatory cytokines and collagen degradation markers in an OA model. PDRN promoted AdMSC proliferation and upregulated ADORA2A expression. AdMSCs exhibited comprehensive anti-inflammatory effects through paracrine effects, and both substances reduced inflammatory gene expression through different mechanisms, potentially enhancing therapeutic effects.

CONCLUSIONS AND RELEVANCE

The results indicate that PDRN is a safe and effective anti-inflammatory material that can be used independently or as an adjuvant for AdMSCs. Although additional research is necessary, this study is significant because it provides a foundation for future research at the cellular level.

Perfluoroalkyl substances exposure and the risk of breast cancer: A nested case-control study in Jinchang Cohort

BACKGROUND

As persistent organic pollutants (POPs), perfluoroalkyl substances (PFAS) may potentially impact human health. Our study aimed to investigate the prospective association between PFAS exposure and the incidence risk of breast cancer in females.

METHODS

By fully following the Jinchang Cohort after a decade, we conducted this nested case-control study with 135 incidence cases of breast cancer (BC) and 540 bias-paired controls. The PFAS levels were tested by baseline serum samples. Conditional logistic regression and a restricted cubic spline model were employed to investigate the BC incidence risks and the dose-response associated with single PFAS component exposure. Furthermore, the Quantile g-computation model (Qgc), random forest model (RFM), and bayesian kernel machine regression models (BKMR) were integrated to estimate the mixed effects of PFAS exposure on the incidence risk of BC.

RESULTS

Exposures to specific PFAS components were positively associated with an increased incidence risk of breast cancer. By grouping the study population into different baseline menopausal statuses, PFHxS, PFNA, PFBA, PFUdA, PFOS, and PFDA demonstrated a similarly positive correlation with BC incidence risks. However, the increased incidence risks of BC associated with PFOA, PFOS, PFUdA, and 9CL-PF3ONS exposure were exclusively found in the premenopausal population. Both BKMR and Qgc revealed that exposure to mixed PFAS was associated with an increased risk of breast cancer, with Qgc specifically indicating an odds ratio (OR) of 2.21 (95% CI: 1.53, 3.19). Random forests showed that PFBA, PFOS, PFHxS, and PFDA emerged as predominant factors potentially influencing breast cancer incidence.

CONCLUSION

Our findings suggest a strong association between PFAS exposure and the incidence of breast cancer. Premenopausal women should exercise more caution regarding PFAS exposure.

Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails

Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O'Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow.

Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks

Botanical formulations are promising candidates for developing new biopesticides that can protect crops from pests and diseases while reducing harm to the environment. These biopesticides can be combined with permeation enhancer compounds to boost their efficacy against pests and fungal diseases. However, finding synergistic combinations of these compounds is challenging due to the large and complex chemical space. In this paper, we propose a novel deep learning method that can predict the synergy of botanical products and permeation enhancers based on in vitro assay data. Our method uses a weighted combination of component feature vectors to represent the input mixtures, which enables the model to handle a variable number of components and to interpret the contribution of each component to the synergy. We also employ an ensemble of interpretation methods to provide insights into the underlying mechanisms of synergy. We validate our method by testing the predicted synergistic combinations in wet-lab experiments and show that our method can discover novel and effective biopesticides that would otherwise be difficult to find. Our method is generalizable and applicable to other domains, where predicting mixtures of chemical compounds is important.

Prediction of drug-target binding affinity based on multi-scale feature fusion

Accurate prediction of drug-target binding affinity (DTA) plays a pivotal role in drug discovery and repositioning. Although deep learning methods are widely used in DTA prediction, two significant challenges persist: (i) how to effectively represent the complex structural information of proteins and drugs; (ii) how to precisely model the mutual interactions between protein binding sites and key drug substructures. To address these challenges, we propose a MSFFDTA (Multi-scale feature fusion for predicting drug target affinity) model, in which multi-scale encoders effectively capture multi-level structural information of drugs and proteins are designed. And then a Selective Cross Attention (SCA) mechanism is developed to filter out the trivial interactions between drug-protein substructure pairs and retain the important ones, which will make the proposed model better focusing on these key interactions and offering insights into their underlying mechanism. Experimental results on two benchmark datasets demonstrate that MSFFDTA is superior to several state-of-the-art methods across almost all comparison metrics. Finally, we provide the ablation and case studies with visualizations to verify the effectiveness and the interpretability of MSFFDTA. The source code is freely available at https://github.com/whitehat32/MSFF-DTA/.

Polypharmacy side effect prediction based on semi-implicit graph variational auto-encoder

Polypharmacy, the use of drug combinations, is an effective approach for treating complex diseases, but it increases the risk of adverse effects. To predict novel polypharmacy side effects based on known ones, many computational methods have been proposed. However, most of them generate deterministic low-dimensional embeddings when modeling the latent space of drugs, which cannot effectively capture potential side effect associations between drugs. In this study, we present SIPSE, a novel approach for predicting polypharmacy side effects. SIPSE integrates single-drug side effect information and drug-target protein data to construct novel drug feature vectors. Leveraging a semi-implicit graph variational auto-encoder, SIPSE models known polypharmacy side effects and generates flexible latent distributions for drug nodes. SIPSE infers the current node distribution by combining the distributions of neighboring nodes with embedding noise. By sampling node embeddings from these distributions, SIPSE effectively predicts polypharmacy side effects between drugs. One key innovation of SIPSE is its incorporation of uncertainty propagation through noise embedding and neighborhood sharing, enhancing its graph analysis capabilities. Extensive experiments on a benchmark dataset of polypharmacy side effects demonstrated that SIPSE significantly outperformed five state-of-the-art methods in predicting polypharmacy side effects.

Knowledge enhanced attention aggregation network for medicine recommendation

The combination of deep learning and the medical field has recently achieved great success, particularly in recommending medicine for patients. However, patients' clinical records often contain repeated medical information that can significantly impact their health condition. Most existing methods for modeling longitudinal patient information overlook the impact of individual diagnoses and procedures on the patient's health, resulting in insufficient patient representation and limited accuracy of medicine recommendations. Therefore, we propose a medicine recommendation model called KEAN, which is based on an attention aggregation network and enhanced graph convolution. Specifically, KEAN can aggregate individual diagnoses and procedures in patient visits to capture significant features that affect patients' diseases. We further incorporate medicine knowledge from complex medicine combinations, reduce drug-drug interactions (DDIs), and recommend medicines that are beneficial to patients' health. The experimental results on the MIMIC-III dataset demonstrate that our model outperforms existing advanced methods, which highlights the effectiveness of the proposed method.

A New Beginning to the Existing Medicines; Repurposing FDA-Approved Drugs for the Neglected Re-Emerging Disease Leptospirosis

Leptospirosis is one of the re-emerging zoonotic diseases, especially in tropical regions. Many antibiotics are used to treat leptospirosis, but there are no scientific evidence-based guidelines or systematic clinical trials for using these drugs. A bioinformatics approach was made to shortlist some Food and Drug Administration (FDA) of the United States of America-approved and currently used drugs for leptospirosis. The existing drugs from the Drug Bank database, which are currently not used for leptospirosis, were selected to identify their target proteins and binding sites using bioinformatics methods. Orthologues of these target proteins were selected from the proteome database of Leptospira. The similar sites and their interactions with the drugs were validated and recommended for use in leptospirosis. Further, the sensitivity of recommended drugs was also validated in vitro. The sequences and structures of these proteins were compared under strictly controlled parameters and shortlisted Gatifloxacin, Imipenem, Latamoxef, Doripenem, Tigecycline, and Lactams as repurposable drugs for leptospirosis. An in vitro validation of the drugs showed significant antileptospiral activity in 12 serovars with low IC50 concentrations and also showed that the IC50 values varied across Leptospira serovars. Further, suitable proteins under the concept of "One Target, Many Drugs" identified DNA gyrase subunit A (Q72WD1), 30S ribosomal protein S9 (Q72U99), and 30S ribosomal protein S12 (Q72UA6), and these proteins were found across the pathogenic, saprophytic, and intermediate species of Leptospira. We describe a method to find repurposable drugs from the approved list that are not currently used to treat leptospirosis and validate them to be taken forward for systematic clinical trials specific to leptospirosis for recommendations in clinical use.

Targeting sphingolipid metabolism in chronic lymphocytic leukemia

Elevated levels of circulating C16:0 glucosylceramides (GluCer) and increased mRNA expression of UDP-glucose ceramide glycosyltransferase (UGCG), the enzyme responsible for converting ceramides (Cer) to GluCer, represent unfavorable prognostic markers in chronic lymphocytic leukemia (CLL) patients. To evaluate the therapeutic potential of inhibiting GluCer synthesis, we genetically repressed the UGCG pathway using in vitro models of leukemic B cells, in addition to UGCG pharmacological inhibition with approved drugs such as eliglustat and ibiglustat, both individually and in combination with ibrutinib, assessed in cell models and primary CLL patient cells. Cell viability, apoptosis, and proliferation were evaluated in vitro, and survival and apoptosis were examined ex vivo. UGCG inhibition efficacy was confirmed by quantifying intracellular sphingolipid levels through targeted lipidomics using mass spectrometry. Other inhibitors of sphingolipid biosynthesis pathways were similarly assessed. Blocking UGCG significantly decreased cell viability and proliferation, highlighting the oncogenic role of UGCG in CLL. The efficient inhibition of UGCG was confirmed by a significant reduction in GluCer intracellular levels. The combination of UGCG inhibitors with ibrutinib demonstrated synergistic effect. Inhibitors that target alternative pathways within sphingolipid metabolism, like sphingosine kinases inhibitor SKI-II, also demonstrated promising therapeutic effects both alone and when used in combination with ibrutinib, reinforcing the oncogenic impact of sphingolipids in CLL cells. Targeting sphingolipid metabolism, especially the UGCG pathway, represents a promising therapeutic strategy and as a combination therapy for potential treatment of CLL patients, warranting further investigation.

Synergistic Enhancement of Chemotherapy-Induced Cell Death and Antitumor Efficacy against Tumoral T-Cell Lymphoblasts by IMMUNEPOTENT CRP

T-cell malignancies, including T-cell acute lymphoblastic leukemia (T-ALL) and T-cell lymphoblastic lymphoma (T-LBL), present significant challenges to treatment due to their aggressive nature and chemoresistance. Chemotherapies remain a mainstay for their management, but the aggressiveness of these cancers and their associated toxicities pose limitations. Immunepotent CRP (ICRP), a bovine dialyzable leukocyte extract, has shown promise in inducing cytotoxicity against various cancer types, including hematological cancers. In this study, we investigated the combined effect of ICRP with a panel of chemotherapies on cell line models of T-ALL and T-LBL (CEM and L5178Y-R cells, respectively) and its impact on immune system cells (peripheral blood mononuclear cells, splenic and bone marrow cells). Our findings demonstrate that combining ICRP with chemotherapies enhances cytotoxicity against tumoral T-cell lymphoblasts. ICRP + Cyclophosphamide (CTX) cytotoxicity is induced through a caspase-, reactive oxygen species (ROS)-, and calcium-dependent mechanism involving the loss of mitochondrial membrane potential, an increase in ROS production, and caspase activation. Low doses of ICRP in combination with CTX spare non-tumoral immune cells, overcome the bone marrow-induced resistance to CTX cell death, and improves the CTX antitumor effect in vivo in syngeneic Balb/c mice challenged with L5178Y-R. This led to a reduction in tumor volume and a decrease in Ki-67 proliferation marker expression and the granulocyte/lymphocyte ratio. These results set the basis for further research into the clinical application of ICRP in combination with chemotherapeutic regimens for improving outcomes in T-cell malignancies.

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases

Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.

CDCS-TCM: A framework based on complex network theory to analyze the causality and dynamic correlation of substances in the metabolic process of traditional Chinese medicine

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine, with the feature of synergistic effects of multi-component, multi-pathway and multi-target, plays an important role in the treatment of cancer, cardiovascular and cerebrovascular diseases, etc. However, chemical components in traditional Chinese medicine are complex and most of the pharmacological mechanisms remain unclear, especially the relationships of chemical components change during the metabolic process.

AIM OF STUDY

Our aim is to provide a method based on complex network theory to analyze the causality and dynamic correlation of substances in the metabolic process of traditional Chinese medicine.

MATERIALS AND METHODS

We proposed a framework named CDCS-TCM to analyze the causality and dynamic correlation between substances in the metabolic process of traditional Chinese medicine. Our method mainly consists two parts. The first part is to discover the local and global causality by the causality network. The second part is to investigate the dynamic correlations and identify the essential substance by dynamic substance correlation network.

RESULTS

We developed a CDCS-TCM method to analyze the causality and dynamic correlation of substances. Using the XiangDan Injection for ischemic stroke as an example, we have identified the important substances in the metabolic process including substance pairs with strong causality and the dynamic changes of the core effector substance clusters.

CONCLUSION

The proposed framework will be useful for exploring the correlations of active ingredients in traditional Chinese medicine more effectively and will provide a new perspective for the elucidation of drug action mechanisms and the new drug discovery.

Association between per- and poly-fluoroalkyl substances and nonalcoholic fatty liver disease: A nested case-control study in northwest China

Per- and poly-fluoroalkyl substances (PFAS) have been reported to have hepatotoxic effects. However, it is unclear whether they are linked to non-alcoholic fatty liver disease (NAFLD). This nested case-control study focused on the epidemiological links between PFAS and the prevalence of NAFLD. We selected 476 new cases of NAFLD and 952 age- and sex-matched controls from the Jinchang cohort population between 2014 and 2019. Serum concentrations of PFAS were measured using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Only PFAS with a detection rate of ≥90 % were included for analysis, which included PFPeA, PFOA, PFNA, PFHxS, PFOS, and 9Cl-PF3ONS. The relationship between single and co-exposure to PFAS and the occurrence of NAFLD was evaluated using conditional logistic regression, Quantile g-computation (QgC), and Bayesian kernel machine regression (BKMR) model. Logistic regression indicated that PFPeA, PFOA, and 9Cl-PF3ONS were positive correlation with the incidence of NAFLD after adjusting for confounders, with odds ratios (OR) and 95 % confidence interval (CI) of 3.13 (95 % CI: 2.53, 3.86), 1.39 (95 % CI: 1.12, 1.73), and 1.41 (95 % CI: 1.20, 1.66), respectively. PFNA, PFHxS, and PFOS were nonlinearly and negatively associated with the incidence of NAFLD, with OR (95 % CI) of 0.53 (0.46, 0.62), 0.83 (0.73, 0.95), and 0.52 (0.44, 0.61), respectively. QgC showed a significant joint effect of PFAS mixture on NAFLD onset (OR: 1.52, 95 % CI: 1.24, 1.88). BKMR showed a weak positive trend between PFAS mixtures and NAFLD incidence. Positive correlations were primarily driven by PFPeA and 9Cl-PF3ONS, while negative correlations were mainly influenced by PFNA and PFOS. The BKMR model also suggested that there was an interaction between PFOS and PFNA and other four PFAS compounds. In conclusion, our findings suggest that individual and co-exposure to PFAS is associated with a risk of NAFLD onset.

ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery

Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations.

Supramolecular interaction in the action of drug delivery systems

Complex diseases and diverse clinical needs necessitate drug delivery systems (DDSs), yet the current performance of DDSs is far from ideal. Supramolecular interactions play a pivotal role in various aspects of drug delivery, encompassing biocompatibility, drug loading, stability, crossing biological barriers, targeting, and controlled release. Nevertheless, despite having some understanding of the role of supramolecular interactions in drug delivery, their incorporation is frequently overlooked in the design and development of DDSs. This perspective provides a brief analysis of the involved supramolecular interactions in the action of drug delivery, with a primary emphasis on the DDSs employed in the clinic, mainly liposomes and polymers, and recognized phenomena in research, such as the protein corona. The supramolecular interactions implicated in various aspects of drug delivery systems, including biocompatibility, drug loading, stability, spatiotemporal distribution, and controlled release, were individually analyzed and discussed. This perspective aims to trigger a comprehensive and systematic consideration of supramolecular interactions in the further development of DDSs. Supramolecular interactions embody the true essence of the interplay between the majority of DDSs and biological systems.

Membrane Permeability Dominates over Electrostatic Interactions in Dictating Drug Transport in Osmotically Shocked Escherichia coli

To combat surging multidrug-resistant Gram-negative bacterial infections, better strategies to improve the efficacy of existing drugs are critical. Because the dual membrane cell envelope is the first line of defense for these bacteria, it is crucial to understand the permeation properties of the drugs through it. Our recent study shows that isosmotic conditions prevent drug permeation inside Gram-negative bacteria, Escherichia coli, while hypoosmotic stress enhances the process. Here, we unravel the reason behind such differential drug penetration. Specifically, we dissect the roles of electrostatic screening and low membrane permeability in the penetration failure of drugs under osmotically balanced conditions. We compare the transport of a quaternary ammonium compound malachite green in the presence of an electrolyte (NaCl) and a wide variety of commonly used organic osmolytes, e.g., sucrose, proline, glycerol, sorbitol, and urea. These osmolytes of different membrane permeability (i.e., nonpermeable sucrose and NaCl, freely permeable urea and glycerol, and partially permeable proline and sorbitol) clarify the role of osmotic stress in cell envelope permeability. The results showcase that under balanced osmotic conditions, drug molecules fail to penetrate inside E. coli cells because of low membrane permeabilities and not because of electrostatic screening imposed by the osmolytes. Contribution of the electrostatic interactions, however, cannot be completely overruled as at osmotically imbalanced conditions, drug transport across the bacterial subcellular compartments is found to be dependent on the osmolytes used.

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations

Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, because of the availability of high-throughput screening data and advances in computational techniques, deep learning (DL) can be a useful tool for the prediction of synergistic drug combinations. In this study, we proposed a multimodal DL framework, MMSyn, for the prediction of synergistic drug combinations. First, features embedded in the drug molecules were extracted: structure, fingerprint, and string encoding. Then, gene expression data, DNA copy number, and pathway activity were used to describe cancer cell lines. Finally, these processed features were integrated using an attention mechanism and an interaction module and then input into a multilayer perceptron to predict drug synergy. Experimental results showed that our method outperformed five state-of-the-art DL methods and three traditional machine learning models for drug combination prediction. We verified that MMSyn achieved superior performance in stratified cross-validation settings using both the drug combination and cell line data. Moreover, we performed a set of ablation experiments to illustrate the effectiveness of each component and the efficacy of our model. In addition, our visual representation and case studies further confirmed the effectiveness of our model. All results showed that MMSyn can be used as a powerful tool for the prediction of synergistic drug combinations.

Thermosensitive polymer prodrug nanoparticles prepared by an all-aqueous nanoprecipitation process and application to combination therapy

Despite their great versatility and ease of functionalization, most polymer-based nanocarriers intended for use in drug delivery often face serious limitations that can prevent their clinical translation, such as uncontrolled drug release and off-target toxicity, which mainly originate from the burst release phenomenon. In addition, residual solvents from the formulation process can induce toxicity, alter the physico-chemical and biological properties and can strongly impair further pharmaceutical development. To address these issues, we report polymer prodrug nanoparticles, which are prepared without organic solvents via an all-aqueous formulation process, and provide sustained drug release. This was achieved by the "drug-initiated" synthesis of well-defined copolymer prodrugs exhibiting a lower critical solution temperature (LCST) and based on the anticancer drug gemcitabine (Gem). After screening for different structural parameters, prodrugs based on amphiphilic diblock copolymers were formulated into stable nanoparticles by all-aqueous nanoprecipitation, with rather narrow particle size distribution and average diameters in the 50-80 nm range. They exhibited sustained Gem release in human serum and acetate buffer, rapid cellular uptake and significant cytotoxicity on A549 and Mia PaCa-2 cancer cells. We also demonstrated the versatility of this approach by formulating Gem-based polymer prodrug nanoparticles loaded with doxorubicin (Dox) for combination therapy. The dual-drug nanoparticles exhibited sustained release of Gem in human serum and acidic release of Dox under accelerated pathophysiological conditions. Importantly, they also induced a synergistic effect on triple-negative breast cancer line MDA-MB-231, which is a relevant cell line to this combination.

DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile

Great efforts have been made over the years to identify novel drug pairs with synergistic effects. Although numerous computational approaches have been proposed to analyze diverse types of biological big data, the pharmacogenomic profiles, presumably the most direct proxy of drug effects, have been rarely used due to the data sparsity problem. In this study, we developed a composite deep-learning-based model that predicts the drug synergy effect utilizing pharmacogenomic profiles as well as molecular properties. Graph convolutional network (GCN) was used to represent and integrate the chemical structure, genetic interactions, drug-target information, and gene expression profiles of cell lines. Insufficient amount of pharmacogenomic data, i.e., drug-induced expression profiles from the LINCS project, was resolved by augmenting the data with the predicted profiles. Our method learned and predicted the Loewe synergy score in the DrugComb database and achieved a better or comparable performance compared to other published methods in a benchmark test. We also investigated contribution of various input features, which highlighted the value of basal gene expression and pharmacogenomic profiles of each cell line. Importantly, DRSPRING (DRug Synergy PRediction by INtegrated GCN) can be applied to any drug pairs and any cell lines, greatly expanding its applicability compared to previous methods.

Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation

BACKGROUND

With increasing antibiotic resistance and regulation, the issue of antibiotic combination has been emphasised. However, antibiotic combination prescribing lacks a rapid identification of feasibility, while its risk of drug interactions is unclear.

METHODS

We conducted statistical descriptions on 16 101 antibiotic coprescriptions for inpatients with bacterial infections from 2015 to 2023. By integrating the frequency and effectiveness of prescriptions, we formulated recommendations for the feasibility of antibiotic combinations. Initially, a machine learning algorithm was utilised to optimise grading thresholds and habits for antibiotic combinations. A feedforward neural network (FNN) algorithm was employed to develop antibiotic combination recommendation model (ACRM). To enhance interpretability, we combined sequential methods and DrugBank to explore the correlation between antibiotic combinations and drug interactions.

RESULTS

A total of 55 antibiotics, covering 657 empirical clinical antibiotic combinations were used for ACRM construction. Model performance on the test dataset showed AUROCs of 0.589-0.895 for various antibiotic recommendation classes. The ACRM showed satisfactory clinical relevance with 61.54-73.33% prediction accuracy in a new independent retrospective cohort. Antibiotic interaction detection showed that the risk of drug interactions was 29.2% for strongly recommended and 43.5% for not recommended. A positive correlation was identified between the level of clinical recommendation and the risk of drug interactions.

CONCLUSIONS

Machine learning modelling of retrospective antibiotic prescriptions habits has the potential to predict antibiotic combination recommendations. The ACRM plays a supporting role in reducing the incidence of drug interactions. Clinicians are encouraged to adopt such systems to improve the management of antibiotic usage and medication safety.

Full-Combinatorial Concentration Gradient Array with 3D Micro/Nanofluidics for Antibiotic Susceptibility Testing

Recent advancements in micro/nanofluidics have facilitated on-chip microscopy of cellular responses in a high-throughput and controlled microenvironment with the desired physicochemical properties. Despite its potential benefits to combination drug discovery, generating a complete combinatorial set of concentration gradients for multiple reagents in an array format remains challenging. The main reason is limited layouts of conventional micro/nanofluidic systems based on two-dimensional channel networks. In this paper, we present a device with three-dimensional (3D) interconnection of micro/nanochannels capable of generating a complete combinatorial set of concentration gradients for two reagents. The device was readily fabricated by laminating a pair of multilayered monolithic films containing a Christmas tree-like mixer, a cell culture chamber array, and through-holes, all within each single film. We assessed the reliable generation of a full-combinatorial concentration gradient array and validated it by using numerical analysis. We applied the proposed device to test the antibiotic susceptibility of bacterial cells in a convenient one-step manner. Furthermore, we explored the potential of the device to accommodate the arrayed complete combinatorial set for two or more drugs, while extending the capabilities of our laminated object manufacturing method for realizing 3D micro/nanofluidic systems.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction

Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.

Synergistic D-Amino Acids Based Antimicrobial Cocktails Formulated via High-Throughput Screening and Machine Learning

Antimicrobial resistance (AMR) from pathogenic bacterial biofilms has become a global health issue while developing novel antimicrobials is inefficient and costly. Combining existing multiple drugs with enhanced efficacy and/or reduced toxicity may be a promising approach to treat AMR. D-amino acids mixtures coupled with antibiotics can provide new therapies for drug-resistance infection with reduced toxicity by lower drug dosage requirements. However, iterative trial-and-error experiments are not tenable to prioritize credible drug formulations, owing to the extremely large number of possible combinations. Herein, a new avenue is provide to accelerate the exploration of desirable antimicrobial formulations via high-throughput screening and machine learning optimization. Such an intelligent method can navigate the large search space and rapidly identify the D-amino acid mixtures with the highest anti-biofilm efficiency and also the synergisms between D-amino acid mixtures and antibiotics. The optimized drug cocktails exhibit high antimicrobial efficacy while remaining non-toxic, which is demonstrated not only from in vitro assessments but also the first in vivo study using a lung infection mouse model.

Screening of traditional medicinal plant extracts and compounds identifies a potent anti-leishmanial diarylheptanoid from Siphonochilus aethiopicus

Available anti-leishmanial drugs are associated with toxic side effects, necessitating the search for safe and effective alternatives. This study is focused on identifying traditional medicinal plant natural products for anti-leishmanial potential and possible mechanism of action. Compounds S and T. cordifolia residual fraction (TC-5) presented the best anti-leishmanial activity (IC50: 0.446 and 1.028 mg/ml) against promastigotes at 48 h and less cytotoxicity to THP-1 macrophages. These test agents elicited increased expression of pro-inflammatory cytokines; TNFα and IL-12. In infected untreated macrophages, NO release was suppressed but was significantly (p < 0.05) increased in infected cells treated with compound S. Importantly, Compound S was found to interact with LdTopoIIdimer in silico, resulting in a likely reduced ability of nucleic acid (dsDNA)-remodelling and, as a result, parasite proliferation in vitro. Thereby, Compound S possesses anti-leishmanial activity and this effect occurs via a Th1-mediated pro-inflammatory response. An increase in NO release and its inhibitory effect on LdTopoII may also contribute to the anti-leishmanial effect of compound S. These results show the potential of this compound as a potential starting point for the discovery of novel anti-leishmanial leads.Communicated by Ramaswamy H. Sarma.

Unlocking the therapeutic potential of drug combinations through synergy prediction using graph transformer networks

Drug combinations are frequently used to treat cancer to reduce side effects and increase efficacy. The experimental discovery of drug combination synergy is time-consuming and expensive for large datasets. Therefore, an efficient and reliable computational approach is required to investigate these drug combinations. Advancements in deep learning can handle large datasets with various biological problems. In this study, we developed a SynergyGTN model based on the Graph Transformer Network to predict the synergistic drug combinations against an untreated cancer cell line expression profile. We represent the drug via a graph, with each node and edge of the graph containing nine types of atomic feature vectors and four bonds features, respectively. The cell lines represent based on their gene expression profiles. The drug graph was passed through the GTN layers to extract a generalized feature map for each drug pairs. The drug pair extracted features and cell-line gene expression profiles were concatenated and subsequently subjected to processing through multiple densely connected layers. SynergyGTN outperformed the state-of-the-art methods, with a receiver operating characteristic area under the curve improvement of 5% on the 5-fold cross-validation. The accuracy of SynergyGTN was further verified through three types of cross-validation tests strategies namely leave-drug-out, leave-combination-out, and leave-tissue-out, resulting in improvement in accuracy of 8%, 1%, and 2%, respectively. The Astrazeneca Dream dataset was utilized as an independent dataset to validate and assess the generalizability of the proposed method, resulting in an improvement in balanced accuracy of 13%. In conclusion, SynergyGTN is a reliable and efficient computational approach for predicting drug combination synergy in cancer treatment. Finally, we developed a web server tool to facilitate the pharmaceutical industry and researchers, as available at: http://nsclbio.jbnu.ac.kr/tools/SynergyGTN/.

Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy

Breast cancer is one of the most common types of cancer in females. While drug combinations have shown potential in breast cancer treatments, identifying new effective drug pairs is challenging due to the vast number of possible combinations among available compounds. Efforts have been made to accelerate the process with in silico predictions. Here, we developed a Boolean model of signaling pathways in breast cancer. The model was tailored to represent five breast cancer cell lines by integrating information about cell-line specific mutations, gene expression, and drug treatments. The models reproduced cell-line specific protein activities and drug-response behaviors in agreement with experimental data. Next, we proposed a calculation of protein synergy scores (PSSs), determining the effect of drug combinations on individual proteins' activities. The PSSs of selected proteins were used to investigate the synergistic effects of 150 drug combinations across five cancer cell lines. The comparison of the highest single agent (HSA) synergy scores between experiments and model predictions from the MDA-MB-231 cell line achieved the highest Pearson's correlation coefficient of 0.58 with a great balance among the classification metrics (AUC = 0.74, sensitivity = 0.63, and specificity = 0.64). Finally, we clustered drug pairs into groups based on the selected PSSs to gain further insights into the mechanisms underlying the observed synergistic effects of drug pairs. Clustering analysis allowed us to identify distinct patterns in the protein activities that correspond to five different modes of synergy: 1) synergistic activation of FADD and BID (extrinsic apoptosis pathway), 2) synergistic inhibition of BCL2 (intrinsic apoptosis pathway), 3) synergistic inhibition of MTORC1, 4) synergistic inhibition of ESR1, and 5) synergistic inhibition of CYCLIN D. Our approach offers a mechanistic understanding of the efficacy of drug combinations and provides direction for selecting potential drug pairs worthy of further laboratory investigation.

Inulin-based formulations as an emerging therapeutic strategy for cancer: A comprehensive review

Cancer stands as the second leading cause of death in the United States (US). Most chemotherapeutic agents exhibit severe adverse effects that are attributed to exposure of drugs to off-target tissues, posing a significant challenge in cancer therapy management. In recent years, inulin, a naturally occurring prebiotic fiber has gained substantial attention for its potential in cancer treatment owing to its multitudinous health values. Its distinctive structure, stability, and nutritional properties position it as an effective adjuvant and carrier for drug delivery in cancer therapy. To address some of the above unmet clinical issues, this review summarizes the recent efforts towards the development of inulin-based nanomaterials and nanocomposites for healthcare applications with special emphasis on the multifunctional role of inulin in cancer therapy as a synergist, signaling molecule, immunomodulatory and anticarcinogenic molecule. Furthermore, the review provides a concise overview of ongoing clinical trials and observational studies associated with inulin-based therapy. In conclusion, the current review offers insights on the significant role of inulin interventions in exploring its potential as a therapeutic agent to treat cancer.

Leishmanicidal and immunomodulatory activity of Terminalia catappa in Leishmania amazonensisin vitro infection

Leishmaniases are infectious-parasitic diseases that impact public health around the world. Antileishmanial drugs presented toxicity and increase in parasitic resistance. Studies with natural products show an alternative to this effect, and several metabolites have demonstrated potential in the treatment of various diseases. Terminalia catappa is a plant species with promising pharmaceutical properties. The objective of this work was to evaluate the therapeutic potential of extracts and fractions of T. catappa on Leishmania amazonensis and investigate the immunomodulatory mechanisms associated with its action. In anti-Leishmania assays, the ethyl acetate fraction exhibited activity against promastigotes (IC50 86.07 ± 1.09 μg/mL) and low cytotoxicity (CC50 517.70 ± 1.68 μg/mL). The ethyl acetate fraction also inhibited the intracellular parasite (IC50 25.74 ± 1.08 μg/mL) with a selectivity index of 20.11. Treatment with T. catappa ethyl acetate fraction did not alter nitrite production by peritoneal macrophages stimulated with L. amazonensis, although there was a decrease in unstimulated macrophages treated at 50 μg/mL (p = 0.0048). The T. catappa ethyl acetate fraction at 100 μg/mL increased TNF-α levels (p = 0.0238) and downregulated HO-1 (p = 0.0030) and ferritin (p = 0.0002) gene expression in L. amazonensis-stimulated macrophages. Additionally, the total flavonoid and ellagic acid content for ethyl acetate fraction was 13.41 ± 1.86 mg QE/g and 79.25 mg/g, respectively. In conclusion, the T. catappa ethyl acetate fraction showed leishmanicidal activity against different forms of L. amazonensis and displayed immunomodulatory mechanisms, including TNF-α production and expression of pro and antioxidant genes.

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction

BACKGROUND

Drug-drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI.

RESULTS

In this paper, we proposed a novel model for DDI prediction based on sequence and substructure features (SSF-DDI) to address these issues. Our model integrates drug sequence features and structural features from the drug molecule graph, providing enhanced information for DDI prediction and enabling a more comprehensive and accurate representation of drug molecules.

CONCLUSION

The results of experiments and case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art DDI prediction models across multiple real datasets and settings. SSF-DDI performs better in predicting DDI involving unknown drugs, resulting in a 5.67% improvement in accuracy compared to state-of-the-art methods.

Targeted Therapeutic Strategies for the Treatment of Cancer

Extensive research is underway to develop new therapeutic strategies to counteract therapy resistance in cancers. This review presents various strategies to achieve this objective. First, we discuss different vectorization platforms capable of releasing drugs in cancer cells. Second, we delve into multitarget therapies using drug combinations and dual anticancer agents. This section will describe examples of multitarget therapies that have been used to treat solid tumors.

Macro CD5L+ deteriorates CD8+T cells exhaustion and impairs combination of Gemcitabine-Oxaliplatin-Lenvatinib-anti-PD1 therapy in intrahepatic cholangiocarcinoma

Intratumoral immune status influences tumor therapeutic response, but it remains largely unclear how the status determines therapies for patients with intrahepatic cholangiocarcinoma. Here, we examine the single-cell transcriptional and TCR profiles of 18 tumor tissues pre- and post- therapy of gemcitabine plus oxaliplatin, in combination with lenvatinib and anti-PD1 antibody for intrahepatic cholangiocarcinoma. We find that high CD8 GZMB+ and CD8 proliferating proportions and a low Macro CD5L+ proportion predict good response to the therapy. In patients with a poor response, the CD8 GZMB+ and CD8 proliferating proportions are increased, but the CD8 GZMK+ proportion is decreased after the therapy. Transition of CD8 proliferating and CD8 GZMB+ to CD8 GZMK+ facilitates good response to the therapy, while Macro CD5L+-CD8 GZMB+ crosstalk impairs the response by increasing CTLA4 in CD8 GZMB+. Anti-CTLA4 antibody reverses resistance of the therapy in intrahepatic cholangiocarcinoma. Our data provide a resource for predicting response of the combination therapy and highlight the importance of CD8+T-cell status conversion and exhaustion induced by Macro CD5L+ in influencing the response, suggesting future avenues for cancer treatment optimization.

Trifluridine for treatment of mpox infection in drug combinations in ophthalmic cell models

The Mpox virus can cause severe disease in the susceptible population with dermatologic and systemic manifestations. Furthermore, ophthalmic manifestations of mpox infection are well documented. Topical trifluridine (TFT) eye drops have been used for therapy of ophthalmic mpox infection in patients, however, its efficacy against mpox virus infection in this scenario has not been previously shown. In the present study, we have established ophthalmic cell models suitable for the infection with mpox virus. We show, that TFT is effective against a broad range of mpox isolates in conjunctival epithelial cells and keratocytes. Further, TFT remained effective against a tecovirimat-resistant virus strain. In the context of drug combinations, a nearly additive effect was observed for TFT combinations with brincidofovir and tecovirimat in conjunctival epithelial cells, while a slight antagonism was observed for both combinations in keratocytes. Altogether, our findings demonstrate TFT as a promising drug for treatment of ophthalmic mpox infection able to overcome tecovirimat resistance. However, conflicting results regarding the effect of drug combinations with approved compounds warrant close monitoring of such use in patients.

Multifunctional nanoparticle-mediated combining therapy for human diseases

Combining existing drug therapy is essential in developing new therapeutic agents in disease prevention and treatment. In preclinical investigations, combined effect of certain known drugs has been well established in treating extensive human diseases. Attributed to synergistic effects by targeting various disease pathways and advantages, such as reduced administration dose, decreased toxicity, and alleviated drug resistance, combinatorial treatment is now being pursued by delivering therapeutic agents to combat major clinical illnesses, such as cancer, atherosclerosis, pulmonary hypertension, myocarditis, rheumatoid arthritis, inflammatory bowel disease, metabolic disorders and neurodegenerative diseases. Combinatorial therapy involves combining or co-delivering two or more drugs for treating a specific disease. Nanoparticle (NP)-mediated drug delivery systems, i.e., liposomal NPs, polymeric NPs and nanocrystals, are of great interest in combinatorial therapy for a wide range of disorders due to targeted drug delivery, extended drug release, and higher drug stability to avoid rapid clearance at infected areas. This review summarizes various targets of diseases, preclinical or clinically approved drug combinations and the development of multifunctional NPs for combining therapy and emphasizes combinatorial therapeutic strategies based on drug delivery for treating severe clinical diseases. Ultimately, we discuss the challenging of developing NP-codelivery and translation and provide potential approaches to address the limitations. This review offers a comprehensive overview for recent cutting-edge and challenging in developing NP-mediated combination therapy for human diseases.

Folic acid-chitosan functionalized polymeric nanocarriers to treat colon cancer

In the present study, polymeric nanoparticles loaded with IRI and quercetin, a p-gp inhibitor, were developed to target folate receptors expressed by colon cancer cells for oral targeted delivery. This work reports the development of PNPs with an entrapment efficiency of 41.26 ± 0.56 % for IRI and 55.83 ± 4.51 for QT. PNPs were further surface modified using chitosan-folic acid conjugates for better targetability to obtain folic acid-chitosan coated nanoparticles. DLS and FeSEM revealed particles in the nanometric size range with spherical morphology, while FTIR and DSC provided details on their structure and encapsulation. In vitro drug release studies confirmed a sustained release pattern of IRI and QT, while cell line studies confirmed the superiority of C-FA-PNPs when tested on Caco2 cells. Pharmacodynamic studies in colon cancer induced rats showed similar efficacy for PNPs and C-FA-PNPs. Further examination from a bio-distribution study in healthy rats, revealed the failure of C-FA-PNPs to deliver the drugs to the colon adequately, while the PNPs improved the available concentration of IRI at the colon by almost 1.8 folds when compared to the available marketed product. Hence, the developed PNP formulation sticks out as a plausible substitute for the intravenous dosage forms of IRI which have been conventionally prevailing.

Intrahippocampal injection of a selective blocker of NMDA receptors containing the GluN2B subunit, Ro25-6981, increases glutamate neurotransmission and induces antidepressant-like effects

Major depressive disorder (MDD) is a serious public health problem, as it is the most common psychiatric disorder worldwide. Antidepressant drugs increase adult hippocampal neurogenesis, which is required to induce some behavioral effects of antidepressants. Adult-born granule cells in the dentate gyrus (DG) and the glutamate receptors subunits 2 (GluN2B) subunit of N-methyl-D-aspartate (NMDA) ionotropic receptors play an important role in these effects. However, the precise neurochemical role of the GluN2B subunit of the NMDA receptor on adult-born GCs for antidepressant-like effects has yet to be elucidated. The present study aims to explore the contribution of the GluN2B-containing NMDA receptors in the ventral dentate gyrus (vDG) to the antidepressant drug treatment using a pharmacological approach. Thus, (αR)-(4-hydroxyphenyl)-(βS)-methyl-4-(phenylmethyl)-1-piperidinepropanol (Ro25-6981), a selective antagonist of the GluN2B subunit, was acutely administered locally into the ventral DG (vDG, 1 μg each side) following a chronic fluoxetine (18 mg/kg/day) treatment-known to increase adult hippocampal neurogenesis-in a mouse model of anxiety/depression. Responses in a neurogenesis-dependent task, the novelty suppressed feeding (NSF), and neurochemical consequences on extracellular glutamate and gamma-aminobutyric acid (GABA) levels in the vDG were measured. Here, we show a rapid-acting antidepressant-like effect of local Ro25-6981 administration in the NSF independent of fluoxetine treatment. Furthermore, we revealed a fluoxetine-independent increase in the glutamatergic transmission in the vDG. Our results suggest behavioral and neurochemical effects of GluN2B subunit independent of serotonin reuptake inhibition.

Posttranslational modifications of keratins and their associated proteins as therapeutic targets in keratin diseases

The keratin cytoskeleton protects epithelia against mechanical, nonmechanical, and physical stresses, and participates in multiple signaling pathways that regulate cell integrity and resilience. Keratin gene mutations cause multiple rare monoallelic epithelial diseases termed keratinopathies, including the skin diseases Epidermolysis Bullosa Simplex (EBS) and Pachyonychia Congenita (PC), with limited available therapies. The disease-related keratin mutations trigger posttranslational modifications (PTMs) in keratins and their associated proteins that can aggravate the disease. Recent findings of drug high-throughput screening have led to the identification of compounds that may be repurposed, since they are used for other human diseases, to treat keratinopathies. These drugs target unique PTM pathways and sites, including phosphorylation and acetylation of keratins and their associated proteins, and have shed insights into keratin regulation and interactions. They also offer the prospect of testing the use of drug mixtures, with the long view of possible beneficial human use coupled with increased efficacy and lower side effects.

Predicting anticancer synergistic drug combinations based on multi-task learning

BACKGROUND

The discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks.

METHODS

In this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features.

RESULTS

Compared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision-recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies.

CONCLUSION

Our study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.

An AI Approach to Identifying Novel Therapeutics for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has a significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain; however, achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches for novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offer the ability to repurpose FDA-approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed, including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations and the ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms that have strong potential to determine novel and effective treatment regimens.

CRISPR-Cas System Is an Effective Tool for Identifying Drug Combinations That Provide Synergistic Therapeutic Potential in Cancers

Despite numerous efforts, the therapeutic advancement for neuroblastoma and other cancer treatments is still ongoing due to multiple challenges, such as the increasing prevalence of cancers and therapy resistance development in tumors. To overcome such obstacles, drug combinations are one of the promising applications. However, identifying and implementing effective drug combinations are critical for achieving favorable treatment outcomes. Given the enormous possibilities of combinations, a rational approach is required to predict the impact of drug combinations. Thus, CRISPR-Cas-based and other approaches, such as high-throughput pharmacological and genetic screening approaches, have been used to identify possible drug combinations. In particular, the CRISPR-Cas system (Clustered Regularly Interspaced Short Palindromic Repeats) is a powerful tool that enables us to efficiently identify possible drug combinations that can improve treatment outcomes by reducing the total search space. In this review, we discuss the rational approaches to identifying, examining, and predicting drug combinations and their impact.

A Multidrug Delivery Microrobot for the Synergistic Treatment of Cancer

Multidrug combination therapy provides an effective strategy for malignant tumor treatment. This paper presents the development of a biodegradable microrobot for on-demand multidrug delivery. By combining magnetic targeting transportation with tumor therapy, it is hypothesized that loading multiple drugs on different regions of a single magnetic microrobot can enhance a synergistic effect for cancer treatment. The synergistic effect of using two drugs together is greater than that of using each drug separately. Here, a 3D-printed microrobot inspired by the fish structure with three hydrogel components: skeleton, head, and body structures is demonstrated. Made of iron oxide (Fe3 O4 ) nanoparticles embedded in poly(ethylene glycol) diacrylate (PEGDA), the skeleton can respond to magnetic fields for microrobot actuation and drug-targeted delivery. The drug storage structures, head, and body, made by biodegradable gelatin methacryloyl (GelMA) exhibit enzyme-responsive cargo release. The multidrug delivery microrobots carrying acetylsalicylic acid (ASA) and doxorubicin (DOX) in drug storage structures, respectively, exhibit the excellent synergistic effects of ASA and DOX by accelerating HeLa cell apoptosis and inhibiting HeLa cell metastasis. In vivo studies indicate that the microrobots improve the efficiency of tumor inhibition and induce a response to anti-angiogenesis. The versatile multidrug delivery microrobot conceptualized here provides a way for developing effective combination therapy for cancer.

Lipid-based nanoparticle-mediated combination therapy for breast cancer management: a comprehensive review

Breast cancer due to the unpredictable and complex etiopathology combined with the non-availability of any effective drug treatment has become the major root of concern for oncologists globally. The number of women affected by the said disease state is increasing at an alarming rate attributed to environmental and lifestyle changes indicating at the exploration of a novel treatment strategy that can eradicate this aggressive disease. So far, it is treated by promising nanomedicine monotherapy; however, according to the numerous studies conducted, the inadequacy of these nano monotherapies in terms of elevated toxicity and resistance has been reported. This review, therefore, puts forth a new multimodal strategic approach to lipid-based nanoparticle-mediated combination drug delivery in breast cancer, emphasizing the recent advancements. A basic overview about the combination therapy and its index is firstly given. Then, the various nano-based combinations of chemotherapeutics involving the combination delivery of synthetic and herbal agents are discussed along with their examples. Further, the recent exploration of chemotherapeutics co-delivery with small interfering RNA (siRNA) agents has also been explained herein. Finally, a section providing a brief description of the delivery of chemotherapeutic agents with monoclonal antibodies (mAbs) has been presented. From this review, we aim to provide the researchers with deep insight into the novel and much more effective combinational lipid-based nanoparticle-mediated nanomedicines tailored specifically for breast cancer treatment resulting in synergism, enhanced antitumor efficacy, and low toxic effects, subsequently overcoming the hurdles associated with conventional chemotherapy.

Mitophagy-promoting agents and their ability to promote healthy-aging

The removal of damaged mitochondrial components through a process called mitochondrial autophagy (mitophagy) is essential for the proper function of the mitochondrial network. Hence, mitophagy is vital for the health of all aerobic animals, including humans. Unfortunately, mitophagy declines with age. Many age-associated diseases, including Alzheimer's and Parkinson's, are characterized by the accumulation of damaged mitochondria and oxidative damage. Therefore, activating the mitophagy process with small molecules is an emerging strategy for treating multiple aging diseases. Recent studies have identified natural and synthetic compounds that promote mitophagy and lifespan. This article aims to summarize the existing knowledge about these substances. For readers' convenience, the knowledge is presented in a table that indicates the chemical data of each substance and its effect on lifespan. The impact on healthspan and the molecular mechanism is reported if known. The article explores the potential of utilizing a combination of mitophagy-inducing drugs within a therapeutic framework and addresses the associated challenges of this strategy. Finally, we discuss the process that balances mitophagy, i.e. mitochondrial biogenesis. In this process, new mitochondrial components are generated to replace the ones cleared by mitophagy. Furthermore, some mitophagy-inducing substances activate biogenesis (e.g. resveratrol and metformin). Finally, we discuss the possibility of combining mitophagy and biogenesis enhancers for future treatment. In conclusion, this article provides an up-to-date source of information about natural and synthetic substances that activate mitophagy and, hopefully, stimulates new hypotheses and studies that promote healthy human aging worldwide.

BDN-DDI: A bilinear dual-view representation learning framework for drug-drug interaction prediction

Drug-drug interactions (DDIs) refer to the potential effects of two or more drugs interacting with each other when used simultaneously, which may lead to adverse reactions or reduced drug efficacy. Accurate prediction of DDIs is a significant concern in recent years. Currently, the drug chemical substructure-based learning method has substantially improved DDIs prediction. However, we notice that most related works ignore the detailed interaction among atoms when extracting the substructure information of drugs. This problem results in incomplete information extraction and may limit the model's predictive ability. In this work, we proposed a novel framework named BDN-DDI (a bilinear dual-view representation learning framework for drug-drug interaction prediction) to infer potential DDIs. In the proposed framework, the encoder consists of six stacked BDN blocks, each of which extracts the feature representation of drug molecules through a bilinear representation extraction layer. The extracted feature is then used to learn embeddings of drug substructures from the single drug learning layer (intra-layer) and the drug-pair learning layer (inter-layer). Finally, the learned embeddings are fed into a decoder to predict DDI events. Based on our experiments, BDN-DDI has an AUROC value of over 99% for the warm-start task. Additionally, it outperformed the state-of-the-art methods by an average of 3.4% for the cold-start tasks. Finally, our method's effectiveness is further validated by visualizing several case studies.

A viral RNA-dependent RNA polymerase inhibitor VV116 broadly inhibits human coronaviruses and has synergistic potency with 3CLpro inhibitor nirmatrelvir

During the ongoing pandemic, providing treatment consisting of effective, low-cost oral antiviral drugs at an early stage of SARS-CoV-2 infection has been a priority for controlling COVID-19. Although Paxlovid and molnupiravir have received emergency approval from the FDA, some side effect concerns have emerged, and the possible oral agents are still limited, resulting in optimized drug development becoming an urgent requirement. An oral remdesivir derivative, VV116, has been reported to have promising antiviral effects against SARS-CoV-2 and positive therapeutic outcomes in clinical trials. However, whether VV116 has broad-spectrum anti-coronavirus activity and potential synergy with other drugs is not clear. Here, we uncovered the broad-spectrum antiviral potency of VV116 against SARS-CoV-2 variants of concern (VOCs), HCoV-OC43, and HCoV-229E in various cell lines. In vitro drug combination screening targeted RdRp and proteinase, highlighting the synergistic effect of VV116 and nirmatrelvir on HCoV-OC43 and SARS-CoV-2. When co-administrated with ritonavir, the combination of VV116 and nirmatrelvir showed significantly enhanced antiviral potency with noninteracting pharmacokinetic properties in mice. Our findings will facilitate clinical treatment with VV116 or VV116+nirmatrelvir combination to fight coronavirus infection.

Tamoxifen-Loaded Eudragit Nanoparticles: Quality by Design Approach for Optimization of Nanoparticles as Delivery System

Nanoparticles have numerous applications as drug carriers in drug delivery. The aim of the study was to produce tamoxifen nanoparticles with a defined size and higher encapsulation for efficient tissue uptake with controlled drug release. The quality by design approach was utilized to produce tamoxifen-loaded Eudragit nanoparticles by identifying the significant process variables using the nanoprecipitation method. The process variables (amount of drug, polymer, and surfactant) were altered to analyze the influence on particle size (PS), % encapsulation efficiency (EE). The results showed that the drug and polymer individually as well as collectively have an impact on PS, while the surfactant has no impact on the PS. The %EE was influenced by the surfactant individually and in interaction with the drug. The linear regression model was endorsed to fit the data showing high R2 values (PS, 0.9146, %EE, 0.9070) and low p values (PS, 0.0004, EE, 0.0005). The PS and EE were confirmed to be 178 nm and 90%, respectively. The nanoparticles were of spherical shape, as confirmed by SEM and TEM. The FTIR confirmed the absence of any incompatibility among the ingredients. The TGA confirmed that the NPs were thermally stable. The in vitro release predicted that the drug release followed Higuchi model.