Drug Pairs Research Articles

Sophora flavescens-Angelica sinensis in the treatment of eczema by inhibiting TLR4/MyD88/NF-κB pathway

Ethnopharmacological relevanceSophora flavescens Ait.-Angelica sinensis(Oliv.) Diels drug pairing (SA) is a transformed drug pairing from Shengui pill, a traditional Chinese medicine prescription in the ninth volume of Traditional Chinese Medicine classic “Gu Jin Yi Jian”, which is famous for clearing heat, moistening dryness, and promoting blood circulation. It is commonly used in the treatment of eczema, a skin condition that causes itching and inflammation. Despite its widespread use, there is still limited research on the mechanism of how SA treats eczema. This paper aims to fill this gap by conducting animal experiments to uncover the mechanism behind SA's therapeutic effects on eczema. Our findings provide a solid foundation for the clinical use of this TCM prescription. Aim of the studyThe basic purpose of this study is to clarify the therapeutic mechanism of Sophora flavescens-Angelica sinensis (SA) in the treatment and control of eczema. Materials and methodsThe chemical compositions of SA were analyzed using HPLC-Q-Orbitrap-MS. In vivo, a mouse model of eczema was created, and the serum levels of TNF-α and IL-1β were quantified using an enzyme-linked immunosorbent assay (ELISA). Hematoxylin and eosin (HE) staining was performed to assess the pathological state of the mouse skin, and immunohistochemical technique (IHC) was employed to estimate the contents of TNF-α, TLR4, and NF-κB semi-quantitatively. The expression levels of TLR4, MyD88, and NF-κB mRNA were determined through real-time quantitative polymerase chain reaction (qRT-PCR). Western Blotting was utilized to identify the protein levels of TLR4, MyD88, and NF-κB in mouse skin tissue. ResultsSA identified 18 active chemicals, some of which were shown in vivo to inhibit the TLR4/MyD88/NF-κB signaling pathway while reducing serum levels of TNF-α and IL-1β, making them ideal agents for the treatment of eczema. ConclusionsSA's anti-inflammatory properties are attributed to its ability to reduce serum levels of TNF-α and IL-1β, likewise inhibit the TLR4/MyD88/NF-κB signaling pathway.

Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2.

Purpose: This study introduces a sophisticated computational pipeline, eVir, designed for the discovery of antiviral drugs based on their interactions within the human protein network. There is a pressing need for cost-effective therapeutics for infectious diseases (e.g., COVID-19), particularly in resource-limited countries. Therefore, our team devised an Artificial Intelligence (AI) system to explore repurposing opportunities for currently used oral therapies. The eVir system operates by identifying pharmaceutical compounds that mirror the effects of antiviral peptides (AVPs)-fragments of human proteins known to interfere with fundamental phases of the viral life cycle: entry, fusion, and replication. eVir extrapolates the probable antiviral efficacy of a given compound by analyzing its established and predicted impacts on the human protein-protein interaction network. This innovative approach provides a promising platform for drug repurposing against SARS-CoV-2 or any virus for which peptide data is available. Methods: The eVir AI software pipeline processes drug-protein and protein-protein interaction networks generated from open-source datasets. eVir uses Node2Vec, a graph embedding technique, to understand the nuanced connections among drugs and proteins. The embeddings are input a Siamese Network (SNet) and MLPs, each tailored for the specific mechanisms of entry, fusion, and replication, to evaluate the similarity between drugs and AVPs. Scores generated from the SNet and MLPs undergo a Platt probability calibration and are combined into a unified score that gauges the potential antiviral efficacy of a drug. This integrated approach seeks to boost drug identification confidence, offering a potential solution for detecting therapeutic candidates with pronounced antiviral potency. Once identified a number of compounds were tested for efficacy and toxicity in lung carcinoma cells (Calu-3) infected with SARS-CoV-2. A lead compound was further identified to determine its efficacy and toxicity in K18-hACE2 mice infected with SARS-CoV-2. Computational Predictions: The SNet confidently differentiated between similar and dissimilar drug pairs with an accuracy of 97.28% and AUC of 99.47%. Key compounds identified through these networks included Zinc, Mebendazole, Levomenol, Gefitinib, Niclosamide, and Imatinib. Notably, Mebendazole and Zinc showcased the highest similarity scores, while Imatinib, Levemenol, and Gefitinib also ranked within the top 20, suggesting their significant pharmacological potentials. Further examination of protein binding analysis using explainable AI focused on reverse engineering the causality of the networks. Protein interaction scores for Mebendazole and Imatinib revealed their effects on notable proteins such as CDPK1, VEGF2, ABL1, and several tyrosine protein kinases. Laboratory Studies: This study determined that Mebendazole, Gefitinib, Topotecan and to some extent Carfilzomib showed conventional drug-response curves, with IC50 values near or below that of Remdesivir with excellent confidence all above R2>0.91, and no cytotoxicity at the IC50 concentration in Calu-3 cells. Cyclosporine A showed antiviral activity, but also unconventional drug-response curves and low R2 which are explained by the non-dose dependent toxicity of the compound. Additionally, Niclosamide demonstrated a conventional drug-response curve with high confidence; however, its inherent cytotoxicity may be a confounding element that misrepresents true antiviral efficacy, by reflecting cellular damage rather than a genuine antiviral action. Remdesivir was used as a control compound and was evaluated in parallel with the submitted test article and had conventional drug-response curves validating the overall results of the assay. Mebendazole was identified from the cell studies to have efficacy at non-toxic concentrations and were further evaluated in mice infected with SARS-CoV-2. Mebendazole administered to K18-hACE2 mice infected with SARS-CoV-2, resulted in a 44.2% reduction in lung viral load compared to non-treated placebo control respectively. There were no significant differences in body weight and all clinical chemistry determinations evaluated (i.e., kidney and liver enzymes) between the different treatment groups. Conclusion: This research underscores the potential of repurposing existing compounds for treating COVID-19. Our preliminary findings underscore the therapeutic promise of several compounds, notably Mebendazole, in both in vitro and in vivo settings against SARS-CoV-2. Several of the drugs explored, especially Mebendazole, are off-label medication; their cost-effectiveness position them as economical therapies against SARS-CoV-2.

Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network.

Drug-drug interactions (DDIs) for emerging drugs offer possibilities for treating and alleviating diseases, and accurately predicting these with computational methods can improve patient care and contribute to efficient drug development. However, many existing computational methods require large amounts of known DDI information, which is scarce for emerging drugs. Here we propose EmerGNN, a graph neural network that can effectively predict interactions for emerging drugs by leveraging the rich information in biomedical networks. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. The edges of the biomedical network are weighted to indicate the relevance for the target DDI prediction. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.

Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis.

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton's tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow's milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs.

Mapping combinatorial drug effects to DNA damage response kinase inhibitors

One fundamental principle that underlies various cancer treatments, such as traditional chemotherapy and radiotherapy, involves the induction of catastrophic DNA damage, leading to the apoptosis of cancer cells. In our study, we conduct a comprehensive dose-response combination screening focused on inhibitors that target key kinases involved in the DNA damage response (DDR): ATR, ATM, and DNA-PK. This screening involves 87 anti-cancer agents, including six DDR inhibitors, and encompasses 62 different cell lines spanning 12 types of tumors, resulting in a total of 17,912 combination treatment experiments. Within these combinations, we analyze the most effective and synergistic drug pairs across all tested cell lines, considering the variations among cancers originating from different tissues. Our analysis reveals inhibitors of five DDR-related pathways (DNA topoisomerase, PLK1 kinase, p53-inducible ribonucleotide reductase, PARP, and cell cycle checkpoint proteins) that exhibit strong combinatorial efficacy and synergy when used alongside ATM/ATR/DNA-PK inhibitors.

Molecular Mechanism of the Asarum-Angelica Drug Pair in the Treatment of Periodontitis Based on Network Pharmacology and Experimental Verification.

(1) To examine the potential mechanism of the Asarum-Angelica drug pair against periodontitis and provide an experimental basis for the treatment of periodontitis with herbal medicine. (2) The core components and core targets of the Asarum-Angelica drug pair in the treatment of periodontitis were detected according to network pharmacology methods. Finally, the effect of the Asarum-Angelica drug pair on osteogenic differentiation was observed in mouse embryonic osteoblast precursor cells. (3) According to the results of network pharmacology, there are 10 potential active ingredients in the Asarum-Angelica drug pair, and 44 potential targets were obtained by mapping the targets with periodontitis treatment. Ten potential active ingredients, such as kaempferol and β-sitosterol, may play a role in treating periodontitis. Cell experiments showed that the Asarum-Angelica drug pair can effectively promote the expression of osteoblast markers alkaline phosphatase (ALP), Runt-related Transcription Factor 2 (RUNX2), and BCL2 mRNA and protein in an inflammatory environment (p < 0.05). (4) Network pharmacology effectively analyzed the molecular mechanism of Asarum-Angelica in the treatment of periodontitis, and the Asarum-Angelica drug pair can promote the differentiation of osteoblasts.

Lipid-lowering effect of drug pair Scutellariae Radix-Coptidis Rhizoma based on lipomics

This study investigated the mechanism of action of Scutellariae Radix-Coptidis Rhizoma(SR-CR) in intervening in non-alcoholic fatty liver disease(NAFLD) in rats based on lipidomics. Thirty-six SD rats were divided into a control group, a model group, SR-CR groups of different doses, and a simvastatin group, with six rats in each group. Rats in the control group were fed on a normal diet, while those in the remaining groups were fed on a high-lipid diet. After four weeks of feeding, drug treatment was carried out and rats were sacrificed after 12 weeks. Serum liver function and lipid indexes were detected using kits, and the pathomorphology of liver tissues was evaluated by hematoxylin-eosin(HE) staining and oil red O staining. Changes in lipid levels in rats were detected using the LC-MS technique. Differential lipid metabolites were screened by multivariate statistical analysis, and lipid metabolic pathways were plotted. The changes in lipid-related protein levels were further verified by Western blot. The results showed that compared with the control group, the model group showed increased levels of alanine aminotransferase(ALT), aspartate aminotransferase(AST), total cholesterol(TC), triglyceride(TG), and low-density lipoprotein cholesterol(LDL-c)(P&lt;0.01), and decreased levels of γ-glutamyl transferase(γ-GT) and high-density lipoprotein cholesterol(HDL-c)(P&lt;0.01), which were significantly recovered by the intervention of SR-CR. HE staining and oil red O staining showed that different doses of SR-CR could reverse the steatosis in the rat liver in a dose-dependent manner. After lipidomics analysis, there were significant differences in lipid metabolism between the model group and the control group, with 54 lipids significantly altered, mainly including glycerolipids, phosphatidylcholine, and sphingolipids. After administration, 44 differential lipids tended to normal levels, which indicated that SR-CR groups of different doses significantly improved the lipid metabolism level in NAFLD rats. Western blot showed that SR-CR significantly decreased TG-synthesis enzyme 1(DGAT1), recombinant lipin 1(LPIN1), fatty acid synthase(FASN), acetyl-CoA carboxylase 1(ACC1), and increased the phosphorylation level of ACC1. These changes significantly decreased the synthesis of TG and increased the rate of its decomposition, which enhanced the level of lipid metabolism in the body and finally achieved the lipid-lowering effect. SR-CR can improve NAFLD by inhibiting the synthesis of fatty acids and TG.

Abstract C176: Cancer-specific AI identifies multi-modal biomarkers of therapeutic response for 1,951 drugs including TNG348, a highly selective USP1 inhibitor

Abstract Understanding drug mechanism-of-action and improving clinical response rates are critical challenges to cancer therapeutic development. We designed a deep learning framework that learns molecular mechanisms of drug sensitivity and resistance from preclinical and/or clinical drug response data. The trained model can then generate biomarker strategies, suggest companion diagnostics, and propose rational drug-drug combinations that increase or restore drug sensitivity. The key innovation is the model’s architecture; the framework learns an embedding that reflects an interpretable relationship between a cellular process and drug response by emulating a tumor cell’s proteomic architecture. We applied the framework to learn genetic determinants of response for 1,951 drugs and 17,386 CRISPR gene knockouts screened in human cancer cell lines. The learned embeddings reflected known and novel mechanisms. RAF inhibitors, MEK inhibitors, and BRAF CRISPR knockout colocalized (P=1.7×10-11) due to their shared dependency on hyperactivation of RAF signaling. CDK4/6 inhibitor efficacy was strongly associated with a set of physically interacting proteins that control G1-to-S transition (P=5.6×10-12). Validating the clinical applicability of the finding, the trained model stratified patient response in a retrospective analysis of 70 ER+ metastatic breast cancer patients treated with the CDK4/6 inhibitor palbociclib (P=0.05, log-rank predicted responsive vs. non-responsive). As a case study, we examined mechanisms of sensitivity and resistance to TNG348 – a potent and highly selective USP1 inhibitor – in non-small cell lung cancer (NSCLC). By integrating genetics, gene expression, and CRISPR screens, the model identified seven genes whose expression explained &amp;gt;50% of the variation in response to TNG348 across 32 held-out NSCLC cell lines (Spearman R=0.74; P=8.7×10-7). The findings extend TNG348’s known relationship with homologous repair to additional protein complexes involved in cell cycle and DNA repair. The model leveraged the new associations to generate a six-biomarker inclusion-exclusion logic that captures both a large population (31.4% of TCGA lung adenocarcinoma patients meet inclusion criteria) and substantially enriches for strong response amongst preclinical models (OR=14.8, P=3.0×10-3). Finally, we asked the model to design rational combinations to overcome acquired resistance mechanisms. Proposed combinations included clinically successful combinations such as RAF+MEK inhibitors and were enriched for synergistic matches in a cell line screen of 986 drug pairings (Spearman rho P=6.6×10-4). These results highlight the promise of explainable AI to learn complex cellular mechanisms-of-action and generate non-obvious biomarker hypothesis and rational drug combination strategies for clinical development. Citation Format: Adam Yaari, Lee McDaniel, Antoine Simoneau, Samuel Meier, Oliver Priebe, Eduardo Farias, Alan Huang, Jannik Andersen, Yi Yu, Maxwell Sherman. Cancer-specific AI identifies multi-modal biomarkers of therapeutic response for 1,951 drugs including TNG348, a highly selective USP1 inhibitor [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C176.

Mechanism of Rhodiola rosea-Euonymus alatus drug pair against rheumatoid arthritis: Network pharmacology and experimental validation.

The present study aimed to explore the potential components and mechanisms of Rhodiola rosea-Euonymus alatus drug pair (TY) that ameliorate rheumatoid arthritis (RA). The main active components, core targets, and important pathways of TY against RA were predicted by network pharmacology analysis. The binding activity between the main active components and the core targets was verified by the molecular docking technique. Collagen-induced arthritis (CIA) rat model and tumor necrosis factor (TNF)-α-induced fibroblast-like synovial cells in human RA (HFLS-RA) model were established, respectively. The core targets were verified by cell counting kit-8 assay, hematoxylin eosin, enzyme-linked immunosorbent assay, real-time polymerase chain reaction, and Western blot analysis, and the therapeutic effect of TY was evaluated. A total of 18 possible components and 34 core targets were obtained by network pharmacology, among which inflammatory response, phosphatidylinositide 3-kinases (PI3K)-AKT and MAPK pathways were involved in the therapeutic effect of TY on RA. The results of molecular docking showed that kaempferol and quercetin had high binding affinity to interleukin (IL)-1β, IL-6, matrix metalloproteinase (MMP)9, and TNF-α. In vivo and in vitro experiments showed that TY dose-dependently inhibited the proliferation of HFLS-RA cells induced by TNF-α, and significantly reduced the paw swelling and arthritis scores in CIA rats. At the same time, TY inhibited the production of inflammatory factors in CIA rat serum and TNF-α-induced HFLS-RA cells. It also decreased the expression of PI3K, phospho-protein kinase B, MMP1, MMP3, MMP9, and increased the protein and mRNA levels of tissue inhibitors of MMPs (TIMP)1 in synovial tissue. TY can inhibit the PI3K/AKT signaling pathway and regulate the balance between MMPs and TIMP, thus playing a therapeutic role in RA.

High Throughput Microfluidics Platform to Assess Synthetic Lethality and Novel Therapeutic Drug Combinations

Molecularly targeted therapies have reshaped cancer treatment in recent decades; however, not all patient cancers are eligible for or effectively treated by such approaches. In addition, resistance mechanisms (both pre-existing and acquired) limit clinical benefit. To these ends, combination therapies involving targeted agents are increasingly being developed for their potential to induce potent synthetic lethalities, bypass drug resistance, promote durable responses, limit adverse side effects via reduced dosing, and expand treatment options. In the context of acute myeloid leukemia (AML) specifically, several efficacious combination therapies have been approved for specific patient subsets, such as all-trans retinoic acid (ATRA) plus arsenic trioxide in the PML-RARA fusion acute promyelocytic subtype and Midostaurin plus Cytarabine and Daunorubicin in FLT3-mutant AML. Despite the availability of these and other therapies, there remains an unmet need for treatments against AML subtypes lacking actionable driver alterations or for which current therapies are insufficient. A major hurdle limiting novel combination therapy discovery is the sheer combinatorics associated with mixing two or more agents (e.g., screening all possible pairs of 100 drugs in a single cell line requires nearly 5,000 combination experiments). Thus, there is a practical need for methodologies that prioritize test compounds among broad candidate sets and high throughput technologies for rapid and efficient screening. We have developed a combination drug screening workflow that integrates machine learning-based predictions of potentially synergistic compound pairs with a droplet microfluidics-based screening platform. Our physical platform, termed the FlowMatrix, contains a series of nano wells arrayed in a 96 x 96 grid (for 9,216 total wells) in which cells are incubated and compounds delivered via emulsified droplets. To facilitate drug combination screening, the FlowMatrix is loaded column-wise with a 96-element library and subsequently loaded a second time along its rows with either the same or an independent drug library. In our current setup, one FlowMatrix can capture complete data for up to 100 unique drug pairs, which includes control, single-agent, and 4x4 dose combination measurements for each pair. Following multi-day incubation, viability for each well is assessed by the number of live cells imaged with fluorescence microscopy. Combinations with high scoring synergy based on Bliss independence criteria are then pushed to validation and further characterization pipelines. We are currently focused on identifying novel combination therapies for AML. Using small molecule libraries of diverse compound classes and machine learning to prioritize initial screening candidates, we have to-date profiled &amp;gt;8,500 AML cell line-drug combinations in our system. This encompasses &amp;gt;2,900 unique drug combinations profiled in 7 distinct AML cell lines. Among ~270 hits that emerged from these screens are established clinical and preclinical AML combination therapies, in addition to candidates that, to the best of our knowledge, are novel in the context of AML. Strong established hits in our screen include ATR inhibition plus Gemcitabine treatment as well as several combinations involving the BCL-2 inhibitor Venetoclax with chemotherapies (Decitabine and Daunorubicin), Quizartinib, Idasanutlin, and mTOR inhibitors. Thus, we have developed an efficient and cost-effective high throughput drug combinations profiling system that has uncovered candidate therapies that may expand treatment options for patients afflicted by AML.

Exploring the mechanism of Dahuang-Tusizi drug pair in the treatment of diabetes nephropathy based on network pharmacology and immune infiltration analysis.

The study aimed to explore the key targets and molecular mechanisms of Dahuang-Tusizi drug pair (DTDP) in the treatment of diabetes nephropathy (DN) based on the GEO database by using network pharmacology combined with molecular docking and immune infiltration. The active components of the DTDP were screened using the Traditional Chinese Medicine Systems Pharmacology database and the Swiss Target Prediction database. The differential genes of DN were retrieved from GEO databases. Next, the intersecting targets of drug and disease were imported into the String database for protein-protein interactions network analysis, and the core targets were identified through topological analysis. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed with the help of the Metascape database and gene set enrichment analysis database. Subsequently, molecular docking was performed to verify the binding activity of the key component and the key target. The Nephroseq V5 database was used to verify the clinical relevance of DN and core genes. Finally, the Using CIBERSORT Algorithm to analyze the immune Infiltration of DN Gene Chip. The network analysis showed that 25 active ingredients of DTDP were associated with 22 targets in DN. The key active ingredients (Sesamin, quercetin, EUPATIN, matrine, beta-sitosterol, isorhamnetin, etc.) and the core targets (JUN, EGF, CD44, FOS, KDR, CCL2, PTGS2, and MMP2) were further identified. Enrichment analysis revealed signaling pathways including TNF, MAPK, and IL-17 signaling pathway. Molecular docking results showed that there was a strong affinity between the key components and core targets. The results of immune infiltration found that the proportion of macrophages in DN tissues was significantly increased. Our findings demonstrated that the characteristics of DTDP in treating DN are "multiple components, multiple targets and multiple pathways." We predicted that DTDP may inhibit inflammation related pathways by regulating key genes, reducing macrophage infiltration. Thus, inhibiting inflammatory response to reduce glomerular damage and delay the development of DN.

Astragaloside IV-PESV Repressed T Cell Immunosuppression by Inhibiting PD-L1 Expression in Prostate Cancer through STAT3 Pathway

Background. Prostate cancer (PCa) is a major threat to men’s health worldwide, and there is an urgent need to find a supportive strategy to improve traditional PD-1/PD-L1 targeted immunotherapy. Our previous research identified astragaloside IV and polypeptide extract from scorpion venom (PESV) as the main active components of the astragalus-scorpion drug pair for treating PCa. In this study, we wanted to continue exploring the modulatory effect of astragaloside IV-PESV on the immune microenvironment of tumors further to investigate the antitumor efficacy mechanism of astragaloside IV-PESV. Methods. First, molecular docking was performed to verify whether astragaloside IV and PESV could bind to STAT3 and PD-L1. Next, we performed mouse tumorigenesis experiments to explore the role of astragaloside IV-PESV. Additionally, we further validated the effects of astragaloside IV-PESV on the STAT3/PD-L1 pathway and immunity by in vitro cellular experiments. Furthermore, we overexpressed STAT3 and validated the effects of overexpression of STAT3 on cellular function, T cell activation, and immune escape in vitro and in vivo. Results. Molecular docking revealed astragaloside IV and PESV bound to STAT3 and PD-L1. Astragaloside IV-PESV led to notable tumor tissue volume and weight repression and inhibited tumor immunity and STAT3/PD-L1 pathway-related protein expressions. In vitro, astragaloside IV-PESV suppressed PD-L1 expression by inhibiting STAT3 signaling to modulate immunity. In contrast, overexpression of STAT3 restored PCa cell proliferation, migration, and invasion inhibition by astragaloside IV-PESV. In addition, overexpression of STAT3 restored the promoting effect of astragaloside IV-PESV on T cell activation. Finally, in vivo experiments further illuminated that overexpression of STAT3 restored the immune escape effect of astragaloside IV-PESV on the tumor. Conclusion. Astragaloside IV-PESV improved T cell immune escape by inhibiting PD-L1 expression in PCa through the STAT3 pathway.

Network pharmacology and molecular docking study on the treatment of polycystic ovary syndrome with angelica sinensis- radix rehmanniae drug pair.

This study aimed to investigate the angelica sinensis - radix rehmanniae (AR) role in polycystic ovary syndrome (PCOS), employing network pharmacology and molecular docking techniques for active ingredient, targets, and pathway prediction. AR active components were obtained through TCMSP platform and literature search. The related targets of AR and PCOS were obtained through the disease and Swiss Target Prediction databases. An "active ingredient-target" network map was constructed using Cytoscape software, and gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis was conducted through Hiplot. Finally, Auto Dock Tools software was used to conduct molecular docking between active ingredients and core targets. The main bioactive ingredients of AR in the treatment of PCOS are acteoside, baicalin, caffeic acid, cistanoside F, geniposide, etc. These ingredients involve 10 core targets, such as SRC, HSP90AA1, STAT3, MAPK1, and JUN. The effect of AR on anti-PCOS mainly involves the AGE-RAGE signaling pathway, Relaxin signaling pathway, TNF signaling pathway, and ErbB signaling pathway. Molecular docking results showed that the main active components and key targets of AR could be stably combined. AR can improve hyperandrogen status, regulate glucose homeostasis, and correct lipid metabolism and other physiological processes through multi-component, multi-target, and multi-pathway. Thus, it could play a significant role in PCOS treatment. The results of our study provide a scientific foundation for basic research and clinical applications of AR for the treatment of PCOS.

Predicting drug-drug interactions based on multi-view and multichannel attention deep learning.

Predicting drug-drug interactions (DDIs) has become a major concern in the drug research field because it helps explore the pharmacological function of drugs and enables the development of new therapeutic drugs. Existing prediction methods simply integrate multiple drug attributes or perform tasks on a biomedical knowledge graph (KG). Though effective, few methods can fully utilize multi-source drug data information. In this paper, a multi-view and multichannel attention deep learning (MMADL) model is proposed, which not only extracts rich drug features containing both drug attributes and drug-related entity information from multi-source databases, but also considers the consistency and complementarity of different drug feature representation learning approaches to improve the effectiveness and accuracy of DDI prediction. A single-layer perceptron encoder is applied to encode multi-source drug information to obtain multi-view drug representation vectors in the same linear space. Then, the multichannel attention mechanism is introduced to obtain the attention weight by adaptively learning the importance of drug features according to their contributions to DDI prediction. Further, the representation vectors of multi-view drug pairs with attention weights are used as inputs of the deep neural network to predict potential DDI. The accuracy and precision-recall curves of MMADL are 93.05 and 95.94, respectively. The results indicate that the proposed method outperforms other state-of-the-art methods.

Molecular docking technology and network pharmacology based on Rhapontici Radix-Cremastrae Pseudobulbus drug pair in treating breast cancer.

Network pharmacology is a bioinformatics-based research strategy for identifying the mechanisms of drugs and promoting drug development. This study used network pharmacology to investigate the mechanism of the Loulu-Cremastrae Pseudobulbus drug pair treating breast cancer (BC). The ingredients and potential targets of the drug pair were searched with Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCSMP). National Center for Biotechnology Information (NCBI) and gene cards were used to search the targets of BC. Networks of "drugs-components-targets" and protein-protein interaction were constructed through Cytoscape. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were carried out through common targets. Using AutoDock tool, molecular docking was performed to verify the binding between key targets and compounds. Finally, we selected 6 active compounds from the drug pair. A total of 61 targets were associated with the drug pair, and 15,295 targets were related to BC. 55 common targets were obtained after the intersection. The key targets included Transcription factor Jun (JUN), Heat shock protein HSP 90-alpha (HSP90AA1), and Caspase-3(CASP3). 327 terms were obtained by GO analysis. 78 pathways (p < 0.05) were identified through KEGG analysis. Molecular docking indicated that important compounds combined well with key targets. Various active compounds, including beta-sitosterol, 2-methoxy-9,10-dihydrophenanthrene-4,5-diol, and stigmasterol, can regulate multiple signaling pathways related to BC, such as the estrogen and prolactin signaling pathways, playing therapeutic roles in BC.

Knowledge and information sources of potential drug–drug interactions of healthcare professionals among Buraydah Hospitals

Background and objectiveDrug–drug interactions (DDI) are known to increase the risk of morbidity and mortality, and adversely affect the patient's quality of life. The study was to assess healthcare professional's (HCP) knowledge of DDIs in general hospitals of Buraydah.MethodsA cross-sectional survey using convenience sampling methods was conducted among 135 healthcare professionals in general hospitals of Buraydah between November and December 2016. The study was carried out after approval and permission from the Regional Research Ethics Committee (November 2016). Respondents were asked to classify 15 drug pairs as 'contraindicated', 'could be used with monitoring', or 'no interaction'. A response option of 'not sure' was also provided. Data were collected using a self-administered questionnaire. The descriptive analysis was done using frequency distribution and percentage for demographic data and other responses to questions. Data were collected, tabulated, and analyzed using Statistical Package for Social Sciences (SPSS) software (version 23). Logistic regression analysis was used to assess the independent variables that affect the HCP knowledge, the significant levels were set at p-value < 0.05.ResultsA total of 135 healthcare professionals were included in the study. The percentage of HCPs who correctly classified the drug pairs ranged from 15 (11.1%) for "Allopurinol + Pyrazinamide" to 90 (66.7%) for "acetaminophen with codeine + amoxicillin". The average number of correctly categorized drug pairs was 5. About one-half of the respondents 73 (54.1%) answered correctly. The level of education was found to be an independent predictor of DDI knowledge. The results from the multivariate analysis indicated that a higher potential DDI knowledge level was associated with pharmacists. Pharmacists had 8.27 times higher DDI knowledge tests than nurses, P value = 0.001. Pharmacists 43(31.9%) were the most cited information source.ConclusionsThe present study revealed that health care professional’s DDI knowledge was inadequate. Level of education was significantly associated with healthcare professionals’ DDI knowledge. Pharmacists were the most cited DDI information source. Healthcare professionals should update their DDI knowledge through continuing education and should improve their familiarity with DDI information sources. These updated educations help to provide the appropriate therapeutic outcomes.

Launch and Post-Launch Prices of Injectable Cancer Drugs in the US: Clinical Benefit, Innovation, Epidemiology, and Competition.

Rising cancer drug prices adversely affect patients' adherence and survival. We aimed to identify and quantify factors associated with launch prices and post-launch price changes of injectable cancer drugs in the US from 2005 to 2023. All anticancer drugs with US FDA approval between 2000 and 2022 were identified in the Drugs@FDA database. The sample was then restricted to cancer drugs covered under Medicare Part B (injectable drugs). Data characterizing each drug's clinical benefits, disease epidemiology, approved indications, competition, and price were obtained from FDA labels, the Global Burden of Disease study, and the Centers for Medicare and Medicaid Services. The association between launch/post-launch prices and collected variables was assessed in random-effects regressions. Of 170 cancer drugs with FDA approval between 2000 and 2022, we identified 66 (39%) injectable cancer drugs with quarterly price data from 2005 to 2023. In 2023, mean prices amounted to $27,688 per month, with an average price increase of 94% from 2005 to 2023. Launch and post-launch price changes were significantly associated with the treated disease epidemiology. A 1% decline in disease incidence was associated with a 0.2511% (p=0.008) increase in launch prices and a 0.0086% (p=0.032) annual increase in post-launch prices. Accordingly, launch prices were 120% (p=0.051) higher for orphan than non-orphan drugs, with 3% (p=0.008) greater annual post-launch price increases. Post-launch prices declined by up to -2% annually as new supplemental indications were approved for the same drug. We found no consistent association between launch/post-launch prices and the drugs' clinical benefit in terms of overall survival, progression-free survival, and tumor response. The market entry of new competitors was not associated with price reductions. 28 of 33 drug pairs within the same class had positive correlation coefficients. Pearson correlation coefficients were high (>0.80) for PD-1/PD-L1 inhibitors, CD38 antibodies, CD20 antibodies, HER2 antibodies, and mTOR inhibitors. Cancer drug prices regularly increase faster than inflation; however, there is no evidence that launch prices and post-launch price changes are aligned with the clinical benefit a drug offers to patients. In particular, patients with rare diseases experience greater price increases for their orphan drugs. There is no evidence that brand-brand competition results in drug price reductions.

MATT-DDI: Predicting multi-type drug-drug interactions via heterogeneous attention mechanisms

The joint use of multiple drugs can result in adverse drug-drug interactions (DDIs) and side effects that harm the body. Accurate identification of DDIs is crucial for avoiding accidental drug side effects and understanding potential mechanisms underlying DDIs. Several computational methods have been proposed for multi-type DDI prediction, but most rely on the similarity profiles of drugs as the drug feature vectors, which may result in information leakage and overoptimistic performance when predicting interactions between new drugs. To address this issue, we propose a novel method, MATT-DDI, for predicting multi-type DDIs based on the original feature vectors of drugs and multiple attention mechanisms. MATT-DDI consists of three main modules: the top k most similar drug pair selection module, heterogeneous attention mechanism module and multi‑type DDI prediction module. Firstly, based on the feature vector of the input drug pair (IDP), k drug pairs that are most similar to the input drug pair from the training dataset are selected according to cosine similarity between drug pairs. Then, the vectors of k selected drug pairs are averaged to obtain a new drug pair (NDP). Next, IDP and NDP are fed into heterogeneous attention modules, including scaled dot product attention and bilinear attention, to extract latent feature vectors. Finally, these latent feature vectors are taken as input of the classification module to predict DDI types. We evaluated MATT-DDI on three different tasks. The experimental results show that MATT-DDI provides better or comparable performance compared to several state-of-the-art methods, and its feasibility is supported by case studies. MATT-DDI is a robust model for predicting multi-type DDIs with excellent performance and no information leakage.

Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication

Background and objectiveThe overall benefits of using clinical decision support systems (CDSSs) can be restrained if physicians inadvertently ignore clinically useful alerts due to “alert fatigue” caused by an excessive number of clinically irrelevant warnings. Moreover, inappropriate drug errors, look-alike/sound-alike (LASA) drug errors, and problem list documentation are common, costly, and potentially harmful. This study sought to evaluate the overall performance of a machine learning-based CDSS (MedGuard) for triggering clinically relevant alerts, acceptance rate, and to intercept inappropriate drug errors as well as LASA drug errors. MethodsWe conducted a retrospective study that evaluated MedGuard alerts, the alert acceptance rate, and the rate of LASA alerts between July 1, 2019, and June 31, 2021, from outpatient settings at an academic hospital. An expert pharmacist checked the suitability of the alerts, rate of acceptance, wrong-drug errors, and confusing drug pairs. ResultsOver the two-year study period, 1,206,895 prescriptions were ordered and a total of 28,536 alerts were triggered (alert rate: 2.36 %). Of the 28,536 alerts presented to physicians, 13,947 (48.88 %) were accepted. A total of 8,014 prescriptions were changed/modified (28.08 %, 8,014/28,534) with the most common reasons being adding and/or deleting diseases (52.04 %, 4,171/8,014), adding and/or deleting drugs (21.89 %, 1,755/8,014) and others (35.48 %, 2,844/ 8,014). However, the rate of drug error interception was 1.64 % (470 intercepted errors out of 28,536 alerts), which equates to 16.4 intercepted errors per 1000 alerted orders. ConclusionThis study shows that machine learning based CDSS, MedGuard, has an ability to improve patients’ safety by triggering clinically valid alerts. This system can also help improve problem list documentation and intercept inappropriate drug errors and LASA drug errors, which can improve medication safety. Moreover, high acceptance of alert rates can help reduce clinician burnout and adverse events.

PRID: Prediction Model Using RWR for Interactions between Drugs.

Drug-drug interactions (DDI) occur because of the unexpected pharmacological effects of drug pairs. Although drug efficacy can be improved by taking two or more drugs in the short term, this may cause inevitable side effects. Currently, multiple drugs are prescribed based on the experience or knowledge of the clinician, and there is no standard database that can be referred to as safe co-prescriptions. Thus, accurately identifying DDI is critical for patient safety and treatment modalities. Many computational methods have been developed to predict DDIs based on chemical structures or biological features, such as target genes or functional mechanisms. However, some features are only available for certain drugs, and their pathological mechanisms cannot be fully employed to predict DDIs by considering the direct overlap of target genes. In this study, we propose a novel deep learning model to predict DDIs by utilizing chemical structure similarity and protein-protein interaction (PPI) information among drug-binding proteins, such as carriers, transporters, enzymes, and targets (CTET) proteins. We applied the random walk with restart (RWR) algorithm to propagate drug CTET proteins across a PPI network derived from the STRING database, which will lead to the successful incorporation of the hidden biological mechanisms between CTET proteins and disease-associated genes. We confirmed that the RWR propagation of CTET proteins helps predict DDIs by utilizing indirectly co-regulated biological mechanisms. Our method identified the known DDIs between clinically proven epilepsy drugs. Our results demonstrated the effectiveness of PRID in predicting DDIs in known drug combinations as well as unknown drug pairs. PRID could be helpful in identifying novel DDIs and associated pharmacological mechanisms to cause the DDIs.